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{1222} is owned by tlh24.{725} is owned by tlh24.{822} is owned by tlh24.{509} is owned by tlh24.{578} is owned by tlh24.{524} is owned by tlh24.{492} is owned by tlh24.{512} is owned by tlh24.{510} is owned by tlh24.{499} is owned by tlh24.{490} is owned by tlh24.{488} is owned by tlh24.{423} is owned by tlh24.{380} is owned by tlh24.
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[0] Loewenstein Y, Seung HS, Operant matching is a generic outcome of synaptic plasticity based on the covariance between reward and neural activity.Proc Natl Acad Sci U S A 103:41, 15224-9 (2006 Oct 10)

[0] Nishida M, Walker MP, Daytime naps, motor memory consolidation and regionally specific sleep spindles.PLoS ONE 2:4, e341 (2007 Apr 4)

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[0] Foster DJ, Wilson MA, Reverse replay of behavioural sequences in hippocampal place cells during the awake state.Nature 440:7084, 680-3 (2006 Mar 30)

[0] Tamaki M, Matsuoka T, Nittono H, Hori T, Fast sleep spindle (13-15 hz) activity correlates with sleep-dependent improvement in visuomotor performance.Sleep 31:2, 204-11 (2008 Feb 1)

[0] Morin A, Doyon J, Dostie V, Barakat M, Hadj Tahar A, Korman M, Benali H, Karni A, Ungerleider LG, Carrier J, Motor sequence learning increases sleep spindles and fast frequencies in post-training sleep.Sleep 31:8, 1149-56 (2008 Aug 1)

[0] Rasch B, Gais S, Born J, Impaired Off-Line Consolidation of Motor Memories After Combined Blockade of Cholinergic Receptors During REM Sleep-Rich Sleep.Neuropsychopharmacology no Volume no Issue no Pages (2009 Feb 4)

[0] Peters J, Schaal S, Reinforcement learning of motor skills with policy gradients.Neural Netw 21:4, 682-97 (2008 May)

[0] Kakade S, Dayan P, Dopamine: generalization and bonuses.Neural Netw 15:4-6, 549-59 (2002 Jun-Jul)

[0] Daw ND, Doya K, The computational neurobiology of learning and reward.Curr Opin Neurobiol 16:2, 199-204 (2006 Apr)

[0] Schultz W, Multiple reward signals in the brain.Nat Rev Neurosci 1:3, 199-207 (2000 Dec)[1] Schultz W, Tremblay L, Hollerman JR, Reward processing in primate orbitofrontal cortex and basal ganglia.Cereb Cortex 10:3, 272-84 (2000 Mar)

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[0] Sergio LE, Hamel-Paquet C, Kalaska JF, Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks.J Neurophysiol 94:4, 2353-78 (2005 Oct)[1] Hatsopoulos NG, Encoding in the motor cortex: was evarts right after all? Focus on "motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks".J Neurophysiol 94:4, 2261-2 (2005 Oct)[2] Cooke JD, Brown SH, Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.J Neurophysiol 63:3, 465-72 (1990 Mar)[3] Almeida GL, Hong DA, Corcos D, Gottlieb GL, Organizing principles for voluntary movement: extending single-joint rules.J Neurophysiol 74:4, 1374-81 (1995 Oct)[4] Gottlieb GL, Latash ML, Corcos DM, Liubinskas TJ, Agarwal GC, Organizing principles for single joint movements: V. Agonist-antagonist interactions.J Neurophysiol 67:6, 1417-27 (1992 Jun)[5] Corcos DM, Agarwal GC, Flaherty BP, Gottlieb GL, Organizing principles for single-joint movements. IV. Implications for isometric contractions.J Neurophysiol 64:3, 1033-42 (1990 Sep)[6] Gottlieb GL, Corcos DM, Agarwal GC, Latash ML, Organizing principles for single joint movements. III. Speed-insensitive strategy as a default.J Neurophysiol 63:3, 625-36 (1990 Mar)[7] Corcos DM, Gottlieb GL, Agarwal GC, Organizing principles for single-joint movements. II. A speed-sensitive strategy.J Neurophysiol 62:2, 358-68 (1989 Aug)[8] Gottlieb GL, Corcos DM, Agarwal GC, Organizing principles for single-joint movements. I. A speed-insensitive strategy.J Neurophysiol 62:2, 342-57 (1989 Aug)[9] Ghez C, Gordon J, Trajectory control in targeted force impulses. I. Role of opposing muscles.Exp Brain Res 67:2, 225-40 (1987)[10] Sainburg RL, Ghez C, Kalakanis D, Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.J Neurophysiol 81:3, 1045-56 (1999 Mar)

[0] Caminiti R, Johnson PB, Galli C, Ferraina S, Burnod Y, Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets.J Neurosci 11:5, 1182-97 (1991 May)

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[0] Townsend BR, Paninski L, Lemon RN, Linear encoding of muscle activity in primary motor cortex and cerebellum.J Neurophysiol 96:5, 2578-92 (2006 Nov)

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ref: -0 tags: neuronal assemblies maass hebbian plasticity simulation austria fMRI date: 02-23-2021 18:49 gmt revision:1 [0] [head]

PMID-32381648 A model for structured information representation in neural networks in the brain

  • Using randomly connected E/I networks, suggests that information can be "bound" together using fast Hebbian STDP.
  • That is, 'assemblies' in higher-level areas reference sensory information through patterns of bidirectional connectivity.
  • These patterns emerge spontaneously following disinihbition of the higher-level areas.
  • Find the results underwhelming, but the discussion is more interesting.
    • E.g. there have been a lot of theoretical and computational-experimental work for how concepts are bound together into symbols or grammars.
    • The referenced fMRI studies are interesting, too: they imply that you can observe the results of structural binding in activity of the superior temporal gyrus.
  • I'm more in favor of dendritic potentials or neuronal up/down states to be a fast and flexible way of maintaining 'symbol membership' --
    • But it's not as flexible as synaptic plasticity, which, obviously, populates the outer product between 'region a' and 'region b' with a memory substrate, thereby spanning the range of plausible symbol-bindings.
    • Inhibitory interneurons can then gate the bindings, per morphological evidence.
    • But but, I don't think anyone has shown that you need protein synthesis for perception, as you do for LTP (modulo AMPAR cycling).
      • Hence I'd argue that localized dendritic potentials can serve as the flexible outer-product 'memory tag' for presence in an assembly.
        • Or maybe they are used primarily for learning, who knows!

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ref: -2017 tags: schema networks reinforcement learning atari breakout vicarious date: 09-29-2020 02:32 gmt revision:2 [1] [0] [head]

Schema networks: zero-shot transfer with a generative causal model of intuitive physics

  • Like a lot of papers, the title has more flash than the actual results.
  • Results which would be state of the art (as of 2017) in playing Atari breakout, then transferring performance to modifications of the game (paddle moved up a bit, wall added in the middle of the bricks, brick respawning, juggling).
  • Schema network is based on 'entities' (objects) which have binary 'attributes'. These attributes can include continuous-valued signals, in which case each binary variable is like a place fields (i think).
    • This is clever an interesting -- rather than just low-level features pointing to high-level features, this means that high-level entities can have records of low-level features -- an arrow pointing in the opposite direction, one which can (also) be learned.
    • The same idea is present in other Vicarious work, including the CAPTCHA paper and more-recent (and less good) Bio-RNN paper.
  • Entities and attributes are propagated forward in time based on 'ungrounded schemas' -- basically free-floating transition matrices. The grounded schemas are entities and action groups that have evidence in observation.
    • There doesn't seem to be much math describing exactly how this works; only exposition. Or maybe it's all hand-waving over the actual, much simpler math.
      • Get the impression that the authors are reaching to a level of formalism when in fact they just made something that works for the breakout task... I infer Dileep prefers the empirical for the formal, so this is likely primarily the first author.
  • There are no perceptual modules here -- game state is fed to the network directly as entities and attributes (and, to be fair, to the A3C model).
  • Entity-attributes vectors are concatenated into a column vector length NTNT , where NN are the number of entities, and TT are time slices.
    • For each entity of N over time T, a row-vector is made of length MRMR , where MM are the number of attributes (fixed per task) and R1R-1 are the number of neighbors in a fixed radius. That is, each entity is related to its neighbors attributes over time.
    • This is a (large, sparse) binary matrix, XX .
  • yy is the vector of actions; task is to predict actions from XX .
    • How is X learned?? Very unclear in the paper vs. figure 2.
  • The solution is approximated as y=XW1¯y = X W \bar{1 } where WW is a binary weight matrix.
    • Minimize the solution based on an objective function on the error and the complexity of ww .
    • This is found via linear programming relaxation. "This procedure monotonically decreases the prediction error of the overall schema network, while increasing its complexity".
      • As it's a issue of binary conjunctions, this seems like a SAT problem!
    • Note that it's not probabilistic: "For this algorithm to work, no contradictions can exist in the input data" -- they instead remove them!
  • Actual behavior includes maximum-product belief propagation, to look for series of transitions that set the reward variable without setting the fail variable.
    • Because the network is loopy, this has to occur several times to set entity variables eg & includes backtracking.

  • Have there been any further papers exploring schema networks? What happened to this?
  • The later paper from Vicarious on zero-shot task transfer are rather less interesting (to me) than this.

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ref: -2015 tags: spiking neural networks causality inference demixing date: 07-22-2020 18:13 gmt revision:1 [0] [head]

PMID-26621426 Causal Inference and Explaining Away in a Spiking Network

  • Rubén Moreno-Bote & Jan Drugowitsch
  • Use linear non-negative mixing plus nose to generate a series of sensory stimuli.
  • Pass these through a one-layer spiking or non-spiking neural network with adaptive global inhibition and adaptive reset voltage to solve this quadratic programming problem with non-negative constraints.
  • N causes, one observation: μ=Σ i=1 Nu ir i+ε \mu = \Sigma_{i=1}^{N} u_i r_i + \epsilon ,
    • r i0r_i \geq 0 -- causes can be present or not present, but not negative.
    • cause coefficients drawn from a truncated (positive only) Gaussian.
  • linear spiking network with symmetric weight matrix J=U TUβI J = -U^TU - \beta I (see figure above)
    • That is ... J looks like a correlation matrix!
    • UU is M x N; columns are the mixing vectors.
    • U is known beforehand and not learned
      • That said, as a quasi-correlation matrix, it might not be so hard to learn. See ref [44].
  • Can solve this problem by minimizing the negative log-posterior function: $$ L(\mu, r) = \frac{1}{2}(\mu - Ur)^T(\mu - Ur) + \alpha1^Tr + \frac{\beta}{2}r^Tr $$
    • That is, want to maximize the joint probability of the data and observations given the probabilistic model p(μ,r)exp(L(μ,r))Π i=1 NH(r i) p(\mu, r) \propto exp(-L(\mu, r)) \Pi_{i=1}^{N} H(r_i)
    • First term quadratically penalizes difference between prediction and measurement.
    • second term, alpha is a L1 regularization term, and third term w beta is a L2 regularization.
  • The negative log-likelihood is then converted to an energy function (linear algebra): W=U TUW = -U^T U , h=U Tμ h = U^T \mu then E(r)=0.5r TWrr Th+α1 Tr+0.5βr TrE(r) = 0.5 r^T W r - r^T h + \alpha 1^T r + 0.5 \beta r^T r
    • This is where they get the weight matrix J or W. If the vectors U are linearly independent, then it is negative semidefinite.
  • The dynamics of individual neurons w/ global inhibition and variable reset voltage serves to minimize this energy -- hence, solve the problem. (They gloss over this derivation in the main text).
  • Next, show that a spike-based network can similarly 'relax' or descent the objective gradient to arrive at the quadratic programming solution.
    • Network is N leaky integrate and fire neurons, with variable synaptic integration kernels.
    • α\alpha translates then to global inhibition, and β\beta to lowered reset voltage.
  • Yes, it can solve the problem .. and do so in the presence of firing noise in a finite period of time .. but a little bit meh, because the problem is not that hard, and there is no learning in the network.

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ref: -0 tags: constitutional law supreme court date: 06-03-2020 01:40 gmt revision:0 [head]

Spent a while this evening reading about Qualified Immunity -- the law that permits government officials (e.g. police officers) immunity when 'doing their jobs'. It's perhaps one root of the George Floyd / racism protests, as it has set a precedent that US police can be violent and get away with it. (This is also related to police unions and collective liability loops... anyway)

The supreme court has the option to take cases challenging the constitutionality of Qualified Immunity, which many on both sides of the political spectrum want them to do.

It 'got' this power via Marbury vs. Madison. M v. M is self-referential genius:

  • They ruled the original action (blocking an appointment) was illegal
  • but the court does not have the power to make these decisions
  • because the congressional law that gave the Supreme Court that power was unconstitutional.
  • Instead, the supreme court has the power to decide if laws (in this case, those governing its jurisdiction) are constitutional.
  • E.g. SCOTUS initiated judicial review & expansion of it's jurisdiction over Congressional law by repealing a law that expanded it's jurisdiction by congress.
  • This was also done while threading the loops to satisfy then-present political pressure (who wanted the original appointment to be illegal) so that they (Thomas Jefferson) were aligned with the increase in power, so the precedent could persist.

As a person curious how systems gain complexity and feedback loops ... so much nerdgasm.

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ref: -0 tags: reinforcement learning distribution DQN Deepmind dopamine date: 03-30-2020 02:14 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-31942076 A distributional code for value in dopamine based reinforcement learning

  • Synopsis is staggeringly simple: dopamine neurons encode / learn to encode a distribution of reward expectations, not just the mean (aka the expected value) of the reward at a given state-action pair.
  • This is almost obvious neurally -- of course dopamine neurons in the striatum represent different levels of reward expectation; there is population diversity in nearly everything in neuroscience. The new interpretation is that neurons have different slopes for their susceptibility to positive and negative rewards (or rather, reward predictions), which results in different inflection points where the neurons are neutral about a reward.
    • This constitutes more optimistic and pessimistic neurons.
  • There is already substantial evidence that such a distributional representation enhances performance in DQN (Deep q-networks) from circa 2017; the innovation here is that it has been extended to experiments from 2015 where mice learned to anticipate water rewards with varying volume, or varying probability of arrival.
  • The model predicts a diversity of asymmetry below and above the reversal point
  • Also predicts that the distribution of reward responses should be decoded by neural activity ... which it is ... but it is not surprising that a bespoke decoder can find this information in the neural firing rates. (Have not examined in depth the decoding methods)
  • Still, this is a clear and well-written, well-thought out paper; glad to see new parsimonious theories about dopamine out there.

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ref: -2002 tags: hashing frequent items count sketch algorithm google date: 03-30-2020 02:04 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

Finding frequent items in data streams

  • Notation:
    • S is a data stream, S=q 1,q 2,...,q n S = q_1, q_2, ..., q_n length n.
    • Each object q iO=o 1,...o mq_i \in O = {o_1, ... o_m} That is, there are m total possible objects (e.g. English words).
    • Object o i o_i occurs n in_i times in S. The o no_n are ordered so that n 1n 2n m n_1 \geq n_2 \geq n_m .
  • Task:
    • Given an input stream S, integer k, and real ε\epsilon
    • Output a list of k elements from S such that each element has n i>(1ε)n k n_i \gt (1-\epsilon)n_k .
      • That is, if the ordering is perfect, n in k n_i \geq n_k , with equality on the last element.
  • Algorithm:
    • h 1,...,h th_1, ..., h_t hashes from object q to buckets 1,...,b{1, ..., b}
    • s 1,...,s ts_1, ..., s_t hashes from object q to 1,+1{-1, +1}
    • For each symbol, add it to the 2D hash array by hashing first with h ih_i , then increment that counter with s is_i .
      • The double-hasihing is to reduce the effect of collisions with high-frequency items.
    • When querying for frequency of a object, hash like others, and take the median over i of h i[q]*s i[q] h_i[q] * s_i[q]
    • t=O(log(nδ))t = O(log(\frac{n}{\delta})) where the algorithm fails with at most probability δ\delta
  • Demonstrate proof of convergence / function with Zipfian distributions with varying exponent. (I did not read through this).
  • Also showed that it's possible to compare these hash-counts directly to see what's changed,or importantly if the documents are different.

Mission: Ultra large-scale feature selection using Count-Sketches
  • Task:
    • Given a labeled dataset (X i,y i)(X_i, y_i) for i1,2,...,ni \in {1,2, ..., n} and X i p,y iX_i \in \mathbb{R}^p, y_i \in \mathbb{R}
    • Find the k-sparse feature vector / linear regression for the mean squares problem min||B|| 0=k||yXΒ|| 2 \frac{min}{||B||_0=k} ||y-X\Beta||_2
      • ||B|| 0=k ||B||_0=k counts the non-zero elements in the feature vector.
    • THE number of features pp is so large that a dense Β\Beta cannot be stored in memory. (X is of course sparse).
  • Such data may be from ad click-throughs, or from genomic analyses ...
  • Use the count-sketch algorithm (above) for capturing & continually updating the features for gradient update.
    • That is, treat the stream of gradient updates, in the normal form g i=2λ(y iX iΒ iX t) tX ig_i = 2 \lambda (y_i - X_i \Beta_i X^t)^t X_i , as the semi-continuous time series used above as SS
  • Compare this with greedy thresholding, Iterative hard thresholding (IHT) e.g. throw away gradient information after each batch.
    • This discards small gradients which may be useful for the regression problem.
  • Works better, but not necessarily better than straight feature hashing (FH).
  • Meh.

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ref: -2015 tags: PaRAC1 photoactivatable Rac1 synapse memory optogenetics 2p imaging mouse motor skill learning date: 10-30-2019 20:35 gmt revision:1 [0] [head]

PMID-26352471 Labelling and optical erasure of synaptic memory traces in the motor cortex

  • Idea: use Rac1, which has been shown to induce spine shrinkage, coupled to a light-activated domain to allow for optogenetic manipulation of active synapses.
  • PaRac1 was coupled to a deletion mutant of PSD95, PSD delta 1.2, which concentrates at the postsynaptic site, but cannot bind to postsynaptic proteins, thus minimizing the undesirable effects of PSD-95 overexpression.
    • PSD-95 is rapidly degraded by proteosomes
    • This gives spatial selectivity.
  • They then exploited the dendritic targeting element (DTE) of Arc mRNA which is selectively targeted and translated in activiated dendritic segments in response to synaptic activation in an an NMDA receptor dependent manner.
    • Thereby giving temporal selectivity.
  • Construct is then PSD-PaRac1-DTE; this was tested on hippocampal slice cultures.
  • Improved sparsity and labelling further by driving it with the Arc promoter.
  • Motor learning is impaired in Arc KO mice; hence inferred that the induction of AS-PaRac1 by the Arc promoter would enhance labeling during learning-induced potentiation.
  • Delivered construct via in-utero electroporation.
  • Observed rotarod-induced learning; the PaRac signal decayed after two days, but the spine volume persisted in spines that showed Arc / DTE hence PA labeled activity.
  • Now, since they had a good label, performed rotarod training followed by (at variable delay) light pulses to activate Rac, thereby suppressing recently-active synapses.
    • Observed both a depression of behavioral performance.
    • Controlled with a second task; could selectively impair performance on one of the tasks based on ordering/timing of light activation.
  • The localized probe also allowed them to image the synapse populations active for each task, which were largely non-overlapping.

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ref: Jackson-2007.01 tags: Fetz neurochip sleep motor control BMI free behavior EMG date: 09-13-2019 02:21 gmt revision:4 [3] [2] [1] [0] [head]

PMID-17021028[0] Correlations Between the Same Motor Cortex Cells and Arm Muscles During a Trained Task, Free Behavior, and Natural Sleep in the Macaque Monkey

  • used their implanted "neurochip" recorder that recorded both EMG and neural activity. The neurochip buffers data and transmits via IR offline. It doesn't have all that much flash onboard - 16Mb.
    • used teflon-insulated 50um tungsten wires.
  • confirmed that there is a strong causal relationship, constant over the course of weeks, between motor cortex units and EMG activity.
    • some causal relationships between neural firing and EMG varied dependent on the task. Additive / multiplicative encoding?
  • this relationship was different at night, during REM sleep, though (?)
  • point out, as Todorov did, that Stereotyped motion imposes correlation between movement parameters, which could lead to spurrious relationships being mistaken for neural coding.
    • Experiments with naturalistic movement are essential for understanding innate, untrained neural control.
  • references {597} Suner et al 2005 as a previous study of long term cortical recordings. (utah probe)
  • during sleep, M1 cells exhibited a cyclical patter on quiescence followed by periods of elevated activity;
    • the cycle lasted 40-60 minutes;
    • EMG activity was seen at entrance and exit to the elevated activity period.
    • during periods of highest cortical activity, muscle activity was completely suppressed.
    • peak firing rates were above 100hz! (mean: 12-16hz).


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ref: -2019 tags: lillicrap google brain backpropagation through time temporal credit assignment date: 03-14-2019 20:24 gmt revision:2 [1] [0] [head]

PMID-22325196 Backpropagation through time and the brain

  • Timothy Lillicrap and Adam Santoro
  • Backpropagation through time: the 'canonical' expansion of backprop to assign credit in recurrent neural networks used in machine learning.
    • E.g. variable rol-outs, where the error is propagated many times through the recurrent weight matrix, W TW^T .
    • This leads to the exploding or vanishing gradient problem.
  • TCA = temporal credit assignment. What lead to this reward or error? How to affect memory to encourage or avoid this?
  • One approach is to simply truncate the error: truncated backpropagation through time (TBPTT). But this of course limits the horizon of learning.
  • The brain may do BPTT via replay in both the hippocampus and cortex Nat. Neuroscience 2007, thereby alleviating the need to retain long time histories of neuron activations (needed for derivative and credit assignment).
  • Less known method of TCA uses RTRL Real-time recurrent learning forward mode differentiation -- δh t/δθ\delta h_t / \delta \theta is computed and maintained online, often with synaptic weight updates being applied at each time step in which there is non-zero error. See A learning algorithm for continually running fully recurrent neural networks.
    • Big problem: A network with NN recurrent units requires O(N 3)O(N^3) storage and O(N 4)O(N^4) computation at each time-step.
    • Can be solved with Unbiased Online Recurrent optimization, which stores approximate but unbiased gradient estimates to reduce comp / storage.
  • Attention seems like a much better way of approaching the TCA problem: past events are stored externally, and the network learns a differentiable attention-alignment module for selecting these events.
    • Memory can be finite size, extending, or self-compressing.
    • Highlight the utility/necessity of content-addressable memory.
    • Attentional gating can eliminate the exploding / vanishing / corrupting gradient problems -- the gradient paths are skip-connections.
  • Biologically plausible: partial reactivation of CA3 memories induces re-activation of neocortical neurons responsible for initial encoding PMID-15685217 The organization of recent and remote memories. 2005

  • I remain reserved about the utility of thinking in terms of gradients when describing how the brain learns. Correlations, yes; causation, absolutely; credit assignment, for sure. Yet propagating gradients as a means for changing netwrok weights seems at best a part of the puzzle. So much of behavior and internal cognitive life involves explicit, conscious computation of cause and credit.
  • This leaves me much more sanguine about the use of external memory to guide behavior ... but differentiable attention? Hmm.

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ref: -2012 tags: DiCarlo Visual object recognition inferior temporal cortex dorsal ventral stream V1 date: 03-13-2019 22:24 gmt revision:1 [0] [head]

PMID-22325196 How Does the Brain Solve Visual Object Recognition

  • James DiCarlo, Davide Zoccolan, Nicole C Rust.
  • Infero-temporal cortex is organized into behaviorally relevant categories, not necessarily retinotopically, as demonstrated with TMS studies in humans, and lesion studies in other primates.
    • Synaptic transmission takes 1-2ms; dendritic propagation ?, axonal propagation ~1ms (e.g. pyramidal antidromic activation latency 1.2-1.3ms), so each layer can use several synapses for computation.
  • Results from the ventral stream computation can be well described by a firing rate code binned at ~ 50ms. Such a code can reliably describe and predict behavior
    • Though: this does not rule out codes with finer temporal resolution.
    • Though anyway: it may be inferential issue, as behavior operates at this timescale.
  • IT neurons' responses are sparse, but still contain information about position and size.
    • They are not narrowly tuned detectors, not grandmother cells; they are selective and complex but not narrow.
    • Indeed, IT neurons with the highest shape selectivities are the least tolerate to changes in position, scale, contrast, and visual clutter. (Zoccolan et al 2007)
    • Position information avoids the need to re-bind attributes with perceptual categories -- no need for syncrhony binding.
  • Decoded IT population activity of ~100 neurons exceeds artificial vision systems (Pinto et al 2010).
  • As in {1448}, there is a ~ 30x expansion of the number of neurons (axons?) in V1 vs the optic tract; serves to allow controlled sparsity.
  • Dispute in the field over primarily hierarchical & feed-forward vs. highly structured feedback being essential for performance (and learning?) of the system.
    • One could hypothesize that feedback signals help lower levels perform inference with noisy inputs; or feedback from higher layers, which is prevalent and manifest (and must be important; all that membrane is not wasted..)
    • DiCarlo questions if the re-entrant intra-area and inter-area communication is necessary for building object representations.
      • This could be tested with optogenetic approaches; since the publication, it may have been..
      • Feedback-type active perception may be evinced in binocular rivalry, or in visual illusions;
      • Yet 150ms immediate object recognition probably does not require it.
  • Authors propose thinking about neurons/local circuits as having 'job descriptions', an metaphor that couples neuroscience to human organization: who is providing feedback to the workers? Who is providing feeback as to job function? (Hinton 1995).
  • Propose local subspace untangling; when this is tacked and tiled, this is sufficient for object perception.
    • Indeed, modern deep convolutional networks behave this way; yet they still can't match human performance (perhaps not sparse enough, not enough representational capability)
    • Cite Hinton & Salakhutdinov 2006.
  • The AND-OR or conv-pooling architecture was proposed by Hubbel and Weisel back in 1962! In their paper's formulatin, they call it a Normalized non-linear model, NLN.
  1. Nonlinearities tend to flatten object manifolds; even with random weights, NLN models tend to produce easier to decode object identities, based on strength of normalization. See also {714}.
  2. NLNs are tuned / become tuned to the statistics of real images. But they do not get into discrimination / perception thereof..
  3. NLNs learn temporally: inputs that occur temporally adjacent lead to similar responses.
    1. But: scaades? Humans saccade 100 million times per year!
      1. This could be seen as a continuity prior: the world is unlikely to change between saccades, so one can infer the identity and positions of objects on the retina, which say can be used to tune different retinotopic IT neurons..
    2. See Li & DiCarlo -- manipulation of image statistics changing visual responses.
  • Regarding (3) above, perhaps attention is a modifier / learning gate?

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ref: -2015 tags: conjugate light electron tomography mouse visual cortex fluorescent label UNC cryoembedding date: 03-11-2019 19:37 gmt revision:1 [0] [head]

PMID-25855189 Mapping Synapses by Conjugate Light-Electron Array Tomography

  • Use aligned interleaved immunofluorescence imaging follwed by array EM (FESEM). 70nm thick sections.
  • Of IHC, tissue must be dehydrated & embedded in a resin.
  • However, the dehydration disrupts cell membranes and ultrastructural details viewed via EM ...
  • Hence, EM microscopy uses osmium tetroxide to cross-link the lipids.
  • ... Yet that also disrupt / refolds the poteins, making IHC fail.
  • Solution is to dehydrate & embed at cryo temp, -70C, where the lipids do not dissolve. They used Lowicryl HM-20.
  • We show that cryoembedding provides markedly improved ultrastructure while still permitting multiplexed immunohistochemistry.

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ref: -2017 tags: calcium imaging seeded iterative demixing light field microscopy mouse cortex hippocampus date: 02-13-2019 22:44 gmt revision:1 [0] [head]

PMID-28650477 Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy

  • Tobias Nöbauer, Oliver Skocek, Alejandro J Pernía-Andrade, Lukas Weilguny, Francisca Martínez Traub, Maxim I Molodtsov & Alipasha Vaziri
  • Cell-scale imaging at video rates of hundreds of GCaMP6 labeled neurons with light-field imaging followed by computationally-efficient deconvolution and iterative demixing based on non-negative factorization in space and time.
  • Utilized a hybrid light-field and 2p microscope, but didn't use the latter to inform the SID algorithm.
  • Algorithm:
    • Remove motion artifacts
    • Time iteration:
      • Compute the standard deviation versus time (subtract mean over time, measure standard deviance)
      • Deconvolve standard deviation image using Richardson-Lucy algo, with non-negativity, sparsity constraints, and a simulated PSF.
      • Yields hotspots of activity, putative neurons.
      • These neuron lcoations are convolved with the PSF, thereby estimating its ballistic image on the LFM.
      • This is converted to a binary mask of pixels which contribute information to the activity of a given neuron, a 'footprint'
        • Form a matrix of these footprints, p * n, S 0S_0 (p pixels, n neurons)
      • Also get the corresponding image data YY , p * t, (t time)
      • Solve: minimize over T ||YST|| 2|| Y - ST||_2 subject to T0T \geq 0
        • That is, find a non-negative matrix of temporal components TT which predicts data YY from masks SS .
    • Space iteration:
      • Start with the masks again, SS , find all sets O kO^k of spatially overlapping components s is_i (e.g. where footprints overlap)
      • Extract the corresponding data columns t it_i of T (from temporal step above) from O kO^k to yield T kT^k . Each column corresponds to temporal data corresponding to the spatial overlap sets. (additively?)
      • Also get the data matrix Y kY^k that is image data in the overlapping regions in the same way.
      • Minimize over S kS^k ||Y kS kT k|| 2|| Y^k - S^k T^k||_2
      • Subject to S k>=0S^k >= 0
        • That is, solve over the footprints S kS^k to best predict the data from the corresponding temporal components T kT^k .
        • They also impose spatial constraints on this non-negative least squares problem (not explained).
    • This process repeats.
    • allegedly 1000x better than existing deconvolution / blind source segmentation algorithms, such as those used in CaImAn

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ref: -0 tags: US employment top 100 bar chart date: 11-12-2018 00:02 gmt revision:1 [0] [head]

After briefly searching the web, I could not find a chart of the top 100 occupations in the US. After downloading the data from the US Bureau of Labor Statistics, made this chart:

Click for full-size.

Surprising how very service heavy our economy is.

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ref: -0 tags: myoelectric EMG recording TMR prosthetics date: 02-13-2017 20:43 gmt revision:0 [head]

PMID: Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

  • General idea: deconvolve a grid-recorded EMG signal to infer the spinal motorneron spikes, and use this to more accurately decode user intention.
  • EMG envelope is still fairly good...

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ref: -0 tags: carbon fiber thread spinning Pasquali Kemere nanotube stimulation date: 02-09-2017 01:09 gmt revision:0 [head]

PMID-25803728 Neural stimulation and recording with bidirectional, soft carbon nanotube fiber microelectrodes.

  • Poulin et al. demonstrated that microelectrodes made solely of CNT fibers22 show remarkable electrochemical activity, sensitivity, and resistance to biofouling compared to conventional carbon fibers when used for bioanalyte detection in vitro.23-25
  • Fibers were insulated with 3 um of block copolymer polystyrene-polybutadiene (PS-b-PBD) (polybutadiene is sythetic rubber)
    • Selected for good properties of biocompatibility, flexibility, resistance to flextural fatigue.
    • Available from Sigma-Aldrich.
    • Custom continuous dip-coating process.
  • 18um diameter, 15 - 20 x lower impedance than equivalently size PtIr.
    • 2.5 - 6x lower than W.
    • In practice, 43um dia, 1450um^2, impedance of 11.2 k; 12.6um, 151k.
  • Charge storage capacity 327 mC / cm^2; PtIr = 1.2 mC/cm^2
  • Wide water window of -1.5V - 1.5V, consistent with noble electrochemical properties of C.
  • Lasts for over 97e6 pulsing cycles beyond the water window, vs 43e6 for PEDOT.
  • Tested via 6-OHDA model of PD disease vs. standard PtIr stimulating electrodes, implanted via 100um PI shuttled attached with PEG.
  • Yes, debatable...
  • Tested out to 3 weeks durability. Appear to function as well or better than metal electrodes.

PMID-23307737 Strong, light, multifunctional fibers of carbon nanotubes with ultrahigh conductivity.

  • Full process:
    1. Dissolve high-quality, 5um long CNT in chlorosulfonic acid (the only known solvent for CNTs)
    2. Filter to remove particles
    3. Extrude liquid crystal dope through a spinneret, 65 or 130um orifice
    4. Into a coagulant, acetone or water
    5. Onto a rotating drum to put tension on the thread & align the CNTs.
    6. Wash in water and dry at 115C.
  • Properties:
    • Tensile strength 1 GPa +- 0.2 GPa.
    • Tensile modulus 120 GPa +- 50, best value 200 GPa
      • Pt: 168 GPa ; Au: 79 GPa.
    • Elongation to break 1.4 %
    • Conductivity: 0.3 MS/m, Iodine doped 5 +- 0.5 MS/m (22 +- 4 microhm cm)
      • Cu: 59.6 MS/m ; Pt: 9.4 MS/m ; Au: 41 MS/m
      • Electrical conductivity drops after annealing @ 600C
      • But does not drop after kinking and repeated mechanical cycling.
  • Theoretical modulus of MWCNT ~ 350 GPa.
  • Fibers well-aligned at ~ 90% the density (measure 1.3 g/cc) of close-packed CNT.

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ref: -0 tags: nanoprobe transmembrane intracellular thiol gold AFM juxtacellular date: 02-06-2017 23:45 gmt revision:3 [2] [1] [0] [head]

PMID-20212151 Fusion of biomimetic stealth probes into lipid bilayer cores

  • Used e-beam evaporation of Cr/Au/Cr 10/10/10 or 10/5/10 onto a Si AFM tip.
    • Approx 200nm diameter; 1800 lipid interaction at the circumference.
  • Exposed the Au in the sandwich via FIB
  • Functionalized the Au with butanethiol or dodecanthiol; former is mobile on the surface, latter is polycrystaline.
    • Butanethiol showed higher adhesion to the synthetic membranes
  • Measured the penetration force & displacement through synthetic multi-layer lipid bilayers.
    • These were made via a custom protocol with 1-stearoyl-2-oleoyl-sn-glycero-3-phosphocholine (SOPC) and cholesterol

PMID-21469728 '''Molecular Structure Influences the Stability of Membrane Penetrating Biointerfaces.

  • Surprisingly, hydrophobicity is found to be a secondary factor with monolayer crystallinity the major determinate of interface strength
  • Previous studies using ellipsometry and IR spectroscopy have shown that alkanethiol self-assembled monolayers display an abrupt transition from a fluid to a crystalline phase between hexanethiol and octanethiol.
    • This suggests the weakening of the membrane stealth probe interface is due to the crystallinity of the molecular surface with fluid, disordered monolayers promoting a high strength interface regime and rigid, crystalline SAMs forming weak interfaces.

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ref: -0 tags: nanopore membrane nanostraws melosh surface adhesion intracellular date: 02-06-2017 23:34 gmt revision:0 [head]

PMID-22166016 Nanostraws for Direct Fluidic Intracellular Access

  1. Used track-etched polycarbonate membranes, which have controlled pore density & ID.
  2. Deposited alumina on the pores & external surfaces using ALD
  3. Then etched away the top alumina
  4. and finally used O2 RIE to etch away the polycarbonate.
  • Show that these nanopores have cytosolic access (via Fluor 488 - hydrazide membrane impermeant dye
  • Also used nanostraws to deliver Co+2 to quench GFP fluorescence.

PMID-24710350, Quantification of nanowire penetration into living cells.

  • We discover that penetration is a rare event: 7.1±2.7% of the nanostraws penetrate the cell to provide cytosolic access for an extended period for an average of 10.7±5.8 penetrations per cell.
  • Using time-resolved delivery, the kinetics of the first penetration event are shown to be adhesion dependent and coincident with recruitment of focal adhesion-associated proteins.
    • Hours for unmodified, 5 minutes for adhesion-promoting surface.
  • Chinese hamster oviary cells expressing GFP, Co+2 quenching, EDTA chelation.
  • To modulate cell adhesion, nanostraw substrates were incubated in 10 μg ml−1 fibronectin, a well-characterized cell adhesion molecule, in addition to the standard polyornithine coating.

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ref: -0 tags: microstimulation rat cortex measurement ICMS spread date: 01-26-2017 02:52 gmt revision:0 [head]

PMID-12878710 Spatiotemporal effects of microstimulation in rat neocortex: a parametric study using multielectrode recordings.

  • Measure using extracellular ephys a spread of ~ 1.3mm from near-threshold microstimulation.
  • Study seems thorough despite limited techniques.

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ref: -0 tags: Kleinfeld vasculature cortex review ischemia perfusion date: 01-22-2017 19:40 gmt revision:3 [2] [1] [0] [head]

PMID-25705966 Robust and fragile aspects of cortical blood flow in relation to the underlying angioarchitecture.

  • "The penetrating arterioles that connect the pial network to the subsurface network are bottlenecks to flow; occlusion of even a single penetrating arteriole results in the death of a 500 μm diameter cylinder of cortical tissue despite the potential for collateral flow through microvessels."
  • The pioneering work of Fox and Raichle [7] suggest that there is simply not enough blood to go around if all areas of the cortex were activated at once.
  • There is strong if only partially understood coupling between neuronal and vascular dysfunction [15]. In particular, vascular disease leads to neurological decline and diminished cognition and memory [16].
  • A single microliter of cortex holds nearly one meter of total vasculature length wow! PMID-23749145
  • Subsurface micro vasculature (not arterioles or venules) is relatively robust to occlusion; figure 4.

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ref: -0 tags: nucleus accumbens caudate stimulation learning enhancement MIT date: 09-20-2016 23:51 gmt revision:1 [0] [head]

Temporally Coordinated Deep Brain Stimulation in the Dorsal and Ventral Striatum Synergistically Enhances Associative Learning

  • Monkeys had to learn to associate an image with one of 4 reward targets.
    • Fixation period, movement period, reward period -- more or less standard task.
    • Blocked trial structure with randomized associations + control novel images + control familiar images.
  • Timed stimulation:
    • Nucleus Accumbens during fixation period
      • Shell not core; non-hedonic in separate test.
    • Caudate (which part -- targeting?) during feedback on correct trials.
  • Performance on stimulated images improved in reaction time, learning rate, and ultimate % correct.
  • Small non-significant improvement in non-stimulated novel image.
  • Wonder how many stim protocols they had to try to get this correct?

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ref: -2012 tags: Emo Todorov contact invariant animation optimization complex motor behavior date: 05-04-2016 17:34 gmt revision:3 [2] [1] [0] [head]

* Watch the [http://homes.cs.washington.edu/~todorov/index.php?video=MordatchSIGGRAPH12&paper=Mordatch,%20SIGGRAPH%202012 movies! Discovery of complex behaviors through contact-invariant optimization]

  • Complex movements tend to have phases within which the set of active contacts (hands, feet) remains invariant (hence can exert forces on the objects they are contacting, or vice versa).
  • Discovering suitable contact sets is the central goal of optimization in our approach.
    • Once this is done, optimizing the remaining aspects of the movement tends to be relatively straightforward.
    • They do this through axillary scalar variables which indicate whether the a contact is active or not, hence whether to enable contact forces.
      • Allows the optimizer to 'realize' that movements should have phases.
      • Also "shapes the energy landscape to be smoother and better behaved"
  • Initial attempts to make these contact axillary variables discrete -- when and where -- which was easy for humans to specify, but made optimization intractable.
    • Motion between contacts was modeled as a continuous feedback system.
  • Instead, the contact variables c ic_i have to be continuous.
  • Contact forces are active only when c ic_i is 'large'.
    • Hence all potential contacts have to be enumerated in advance.
  • Then, parameterize the end effector (position) and use inverse kinematics to figure out joint angles.
  • Optimization:
    • Break the movement up into a predefined number of phases, equal duration.
    • Interpolate end-effector with splines
    • Physics constraints are 'soft' -- helps the optimizer : 'powerful continuation methods'
      • That is, weight different terms differently in phases of the optimization process.
      • Likewise, appendages are allowed to stretch and intersect, with a smooth cost.
    • Contact-invariant cost penalizes distortion and slip (difference between endpoint and surface, measured normal, and velocity relative to contact point)
      • Contact point is also 'soft' and smooth via distance-normalized weighting.
    • All contact forces are merged into a f 6f \in \mathbb{R}^6 vector, which includes both forces and torques. Hence contact force origin can move within the contact patch, which again makes the optimization smoother.
    • Set τ(q,q˙,q¨)=J(q) Tf+Bu\tau(q, \dot{q}, \ddot{q}) = J(q)^T f + B u where J(q) T J(q)^T maps generalize (endpoint) velocities to contact-point velocities, and f above are the contact-forces. BB is to map control forces uu to the full space.
    • τ(q,q˙,q¨)=M(q)q˙+C(q,q˙)q˙+G(q)\tau(q, \dot{q}, \ddot{q}) = M(q)\dot{q} + C(q, \dot{q})\dot{q} + G(q) -- M is inertia matrix, C is Coriolis matrix, g is gravity.
      • This means: forces need to add to zero. (friction ff + control uu = inertia + coriolis + gravity)
    • Hence need to optimize ff and uu .
      • Use friction-cone approximation for non-grab (feet) contact forces.
    • These are optimized within a quadratic programming framework.
      • LBFGS algo.
      • Squared terms for friction and control, squared penalization for penetrating and slipping on a surface.
    • Phases of optimization (continuation method):
      • L(s)=L CI(s)+L physics(s)+L task(s)+L hint(s)L(s) = L_{CI}(s) + L_{physics}(s) + L_{task}(s) + L_{hint}(s)
      • task term only: wishful thinking.
      • all 4 terms, physcics lessened -- gradually add constraints.
      • all terms, no hint, full physics.
  • Total time to simulate 2-10 minutes per clip (only!)
  • The equations of the paper seem incomplete -- not clear how QP eq fits in with the L(s)L(s) , and how c ic_i fits in with J(q) Tf+BuJ(q)^T f + B u

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ref: -0 tags: deep reinforcement learning date: 04-12-2016 17:19 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

Prioritized experience replay

  • In general, experience replay can reduce the amount of experience required to learn, and replace it with more computation and more memory – which are often cheaper resources than the RL agent’s interactions with its environment.
  • Transitions (between states) may be more or less
    • surprising (does the system in question have a model of the environment? It does have a model of the state & action expected reward, as it's Q-learning.
    • redundant, or
    • task-relevant
  • Some sundry neuroscience links:
    • Sequences associated with rewards appear to be replayed more frequently (Atherton et al., 2015; Ólafsdóttir et al., 2015; Foster & Wilson, 2006). Experiences with high magnitude TD error also appear to be replayed more often (Singer & Frank, 2009 PMID-20064396 ; McNamara et al., 2014).
  • Pose a useful example where the task is to learn (effectively) a random series of bits -- 'Blind Cliffwalk'. By choosing the replayed experiences properly (via an oracle), you can get an exponential speedup in learning.
  • Prioritized replay introduces bias because it changes [the sampled state-action] distribution in an uncontrolled fashion, and therefore changes the solution that the estimates will converge to (even if the policy and state distribution are fixed). We can correct this bias by using importance-sampling (IS) weights.
    • These weights are the inverse of the priority weights, but don't matter so much at the beginning, when things are more stochastic; they anneal the controlling exponent.
  • There are two ways of selecting (weighting) the priority weights:
    • Direct, proportional to the TD-error encountered when visiting a sequence.
    • Ranked, where errors and sequences are stored in a data structure ordered based on error and sampled 1/rank\propto 1 / rank .
  • Somewhat illuminating is how the deep TD or Q learning is unable to even scratch the surface of Tetris or Montezuma's Revenge.

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ref: -0 tags: street fighting mathematics Sanjoy Mahajan date: 10-04-2015 23:09 gmt revision:0 [head]



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ref: -0 tags: standard enthalpy chemicals list pdf date: 06-25-2015 00:09 gmt revision:1 [0] [head]

Standard thermodynamic properties of chemical substances

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ref: -0 tags: Peter Ledochowitsch ECoG parylene fabrication MEMS date: 09-25-2014 16:54 gmt revision:0 [head]

IEEE-5734604 (pdf) Fabrication and testing of a large area, high density, parylene MEMS µECoG array

  • Details 5-layer platinum parylene process for high density ECoG arrays.

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ref: -0 tags: intracortical utah array fabrication MEMS Normann date: 08-14-2014 01:35 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-1937509 A silicon-based, three-dimensional neural interface: manufacturing processes for an intracortical electrode array.

  • Campbell PK1, Jones KE, Huber RJ, Horch KW, Normann RA. (1991)
  • One of (but not the) first papers describing their methods / idea (I think).
  • First conf paper: {1294} (1988)
  • later adopted glass frit insulator --

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ref: -0 tags: ovipositor wasp fig needle insertion SEM date: 05-29-2014 19:58 gmt revision:0 [head]

Biomechanics of substrate boring by fig wasps

  • Lakshminath Kundanati and Namrata Gundiah 2014

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ref: -0 tags: perl directory descent script remove date: 01-10-2014 06:12 gmt revision:0 [head]

Simple perl scrip for removing duplicate files within sub-directories of a known depth:

#!/usr/bin/perl -w

@files = <*>;
foreach $file (@files) {
	@files2 = <$file/*>;
	foreach $file2 (@files2) {
		print $file2 . "\n";
		`rm -rf $file2/*_1.jpg`; 
		`rm -rf $file2/*_2.jpg`; 

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ref: -0 tags: shape memory polymers neural interface thiolene date: 12-06-2013 22:55 gmt revision:0 [head]

PMID-23852172 A comparison of polymer substrates for photolithographic processing of flexible bioelectronics

  • Describe the deployment of shape-memory polymers for a neural interface
    • Thiol-ene/acrrylate network (see figures)
    • Noble metals react strongly to the thiols, yielding good adhesion.
  • Cr/Au thin films.
  • Devices change modulus as they absorb water; clever!
  • Transfer by polymerization patterning of electrodes (rather than direct sputtering).
    • This + thiol adhesion still might not be sufficient to prevent micro-cracks.
  • "Neural interfaces fabricated on thiol-ene/acrylate substrates demonstrate long-term fidelity through both in vitro impedance spectroscopy and the recording of driven local field potentials for 8 weeks in the auditory cortex of laboratory rats. "
  • Impedance decreases from 1M @ 1kHz to ~ 100k over the course of 8 weeks. Is this acceptable? Seems like the insulator is degrading (increased capacitance; they do not show phase of impedance)
  • PBS uptake @ 37C:
    • PI seems to have substantial PBS uptake -- 2%
    • PDMS the lowest -- 0.22%
    • PEN (polyethelene napathalate) -- 0.36%
    • Thiol-ene/acrylate 2.19%
  • Big problem is that during photolithographic processing all the shape-memory polymers go through Tg, and become soft/rubbery, making thin metal film adhesion difficult.
    • Wonder if you could pattern more flexible materials, e.g. carbon nanotubes (?)
  • Good paper, many useful references!

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ref: -0 tags: woodchuck post-translational regulatory element date: 09-30-2013 18:52 gmt revision:2 [1] [0] [head]

PMID-10074136 Woodchuck hepatitis virus posttranscriptional regulatory element enhances expression of transgenes delivered by retroviral vectors 1999

  • "These results demonstrate that the WPRE significantly improves the performance of retroviral vectors and emphasize that posttranscriptional regulation of gene expression should be taken into account in the design of gene delivery systems."
  • Only useful in Cre recombinase sites (? I don't know much about this!)
  • used in e.g {1255}

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ref: -0 tags: parylene microchannel micromolding glass transition temperature microfluidics date: 06-28-2013 17:34 gmt revision:3 [2] [1] [0] [head]

Parylene micromolding, a rapid low-cost fabrication method for parylene microchannel

  • doi:10.1016/j.snb.2003.09.038
  • Hong-Seok Noha∗ , Yong Huangb, Peter J. Hesketha Clemson
  • Parylene properties:
    • Glass transition temperature <90C; c.f. {1247}
    • Melting point 290C
    • Oxidation in air at 120C
    • Thermal bonding here at 200C in a vacuum oven @ 24MPa.

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ref: -0 tags: plasma etch removal parylene DRIE date: 05-28-2013 18:47 gmt revision:2 [1] [0] [head]

Plasma removal of Parylene C

  • Ellis Meng, Po-Ying Li and Yu-Chong Tai USC / Caltech
  • Technics O2 plasma etch works, as do DRIE / RIE etch; all offer varying degrees of anisotropy, with the more intricate processes offering straighter sidewalls.
  • Suggested parameters for O2 etch is 200sccm / 200W.
  • Etch will be somewhat isotropic -- top of photoresist will be etched away, leading to ~15deg sloped sidewalls.
    • Hence, small parylene features will be narrowed by the 02 plasma.

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ref: Leung-2008.08 tags: biocompatibility alginate tissue response immunochemistry microglia insulation spin coating Tresco recording histology MEA date: 01-28-2013 21:19 gmt revision:4 [3] [2] [1] [0] [head]

PMID-18485471[0] Characterization of microglial attachment and cytokine release on biomaterials of differing surface chemistry

  • The important result is that materials with low protein-binding (e.g. alginate) have fewer bound microglia, hence better biocompatibility. It also seems to help if the material is highly hydrophilic.
    • Yes alginate is made from algae.
  • Used Michigan probes for implantation.
  • ED1 = pan-macrophage marker.
    • (quote:) Quantification of cells on the surface indicated that the number of adherent microglia appeared higher on the smooth side of the electrode compared to the grooved, recording site side (Fig. 2B), and declined with time. However, at no point were electrodes completely free of attached and activated microglial cells nor did these cells disappear from the interfacial zone along the electrode tract.
    • but these were not coated with anything new .. ???


[0] Leung BK, Biran R, Underwood CJ, Tresco PA, Characterization of microglial attachment and cytokine release on biomaterials of differing surface chemistry.Biomaterials 29:23, 3289-97 (2008 Aug)

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ref: Kim-2004.05 tags: histology electrode immune response Tresco hollow fiber membranes GFAP vimentin ED1 date: 01-28-2013 03:08 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

PMID-14741588[0] Chronic response of adult rat brain tissue to implants anchored to the skull.

  • The increase in tissue reactivity observed with transcranially implanted HFMs may be influenced by several mechanisms including chronic contact with the meninges and possibly motion of the device within brain tissue.
  • Broadly speaking, our results suggest that any biomaterial, biosensor or device that is anchored to the skull and in chronic contact with meningeal tissue will have a higher level of tissue reactivity than the same material completely implanted within brain tissue.
  • See also [1]
  • Could slice through the hollow fiber membrane for histology. (as we shall).
  • Good list of references.


[0] Kim YT, Hitchcock RW, Bridge MJ, Tresco PA, Chronic response of adult rat brain tissue to implants anchored to the skull.Biomaterials 25:12, 2229-37 (2004 May)
[1] Biran R, Martin DC, Tresco PA, The brain tissue response to implanted silicon microelectrode arrays is increased when the device is tethered to the skull.J Biomed Mater Res A 82:1, 169-78 (2007 Jul)

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ref: Chestek-2009.09 tags: BMI problems address critique spike sorting Shenoy date: 01-23-2013 02:23 gmt revision:3 [2] [1] [0] [head]

IEEE-5332822 (pdf) Neural prosthetic systems: Current problems and future directions

  • Where there is unlikely to be improvements: spike sorting and spiking models.
  • Where there are likely to be dramatic improvements: non-stationarity of recorded waveforms, limitations of a linear mappings between neural activity and movement kinematics, and the low signal to noise ratio of the neural data.
  • Compare different sorting methods: threshold, single unit, multiunit, relative to decoding.
  • Plot waveform changes over an hour -- this contrasts with earlier work (?) {1032}
  • Figure 5: there is no obvious linear transform between neural activity and the kinematic parameters.
  • Suggest that linear models need to be replaced by the literature of how primates actually make reaches.
  • Discuss that offline performance is not at all the same as online; in the latter the user can learn and adapt on the fly!


Chestek, C.A. and Cunningham, J.P. and Gilja, V. and Nuyujukian, P. and Ryu, S.I. and Shenoy, K.V. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE 3369 -3375 (2009)

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ref: -0 tags: artificial intelligence projection episodic memory reinforcement learning date: 08-15-2012 19:16 gmt revision:0 [head]

Projective simulation for artificial intelligence

  • Agent learns based on memory 'clips' which are combined using some pseudo-bayesian method to trigger actions.
    • These clips are learned from experience / observation.
    • Quote: "..more complex behavior seems to arise when an agent is able to “think for a while” before it “decides what to do next.” This means the agent somehow evaluates a given situation in the light of previous experience, whereby the type of evaluation is different from the execution of a simple reflex circuit"
    • Quote: "Learning is achieved by evaluating past experience, for example by simple reinforcement learning".
  • The forward exploration of learned action-stimulus patterns is seemingly a general problem-solving strategy (my generalization).
  • Pretty simple task:
    • Robot can only move left / right; shows a symbol to indicate which way it (might?) be going.

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ref: Freire-2011.01 tags: Nicolelis BMI electrodes immune respones immunohistochemistry chronic arrays rats 2011 MEA histology date: 06-29-2012 01:20 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-22096594[0] Comprehensive analysis of tissue preservation and recording quality from chronic multielectrode implants.

  • Says what might be expected: tungsten microelectrode arrays work, though the quality gradually declines over 6 months.
  • Histological markers correlated well with recording performance.
  • Shows persistent glial activation around electrode sites + cell body hypertropy.
    • Suggest that loss in recording quality may be due to glial encapsulation.
  • References
    • Szarowski et al 2003 {1028}
    • Ward et al 2009
  • Histology:
    • NADPH-d: nicotinamide adenine dinucleotide phosphate-diaphorase, via beta-NADP
    • CO: cytochrome oxidase, via diamnibenzidine DAB, cytochrome c and catalase.
      • both good for staining cortical layers; applied in a standard buffered solution and monitored to prevent overstaining.
  • Immunohistochemistry:
    • Activated microglia with ED-1 antibody.
    • Astrocytes labeled with glial fibrillary acid protein.
    • IEG with an antibody against EGR-1, 'a well-known marker of calcium dependent neuronal activity'
    • Neurofilament revealed using a monoclonal NF-M antibody.
    • Caspace-3 with the associated antibody
    • Details the steps for immunostaining -- wash, blocknig buffer, addition of the antibody in diluted blocking solution (skim milk) overnight, wash again, incubate in biotinylated secondary antibody, wash again, incubate in avidin-biotin-peroxidase solution.
    • Flourescent immunohistochemistry had biotynlation replaced with alexa Fluor 488-conjugated horse anti-mouse and Alexa Fluor 594-conjugated goat anti-rabbit overnight.


[0] Freire MA, Morya E, Faber J, Santos JR, Guimaraes JS, Lemos NA, Sameshima K, Pereira A, Ribeiro S, Nicolelis MA, Comprehensive analysis of tissue preservation and recording quality from chronic multielectrode implants.PLoS One 6:11, e27554 (2011)

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ref: -0 tags: hippocampus theta oscillations memory date: 03-18-2012 18:09 gmt revision:0 [head]

PMID-21696996 The hippocampus: hub of brain network communication for memory

  • Their hypothesis: memory encoding is dominated by theta oscillations 6-10 Hz; during inactivity, hippocampal neurons burst synchronously, creating sharp waves, theoretically supporting memory consolidation.
  • (They claim): to date there is no generally accepted theoretical view on memory consolidation.
  • Generally it seems to shift from hippocampus to neocortex, but still, evidence is equivocal. (Other than HM & other human evidence?)
  • Posit a theory based on excitation ramps of reverse-replay, which seems a bit fishy to me (figure 3).
  • Didn't know this: replay in visual and PFC can be so precise that it preserves detailed features of the crosscorrelograms between neurons. [58, 65, 81].

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ref: bookmark-0 tags: basal ganglia dopamine reinforcement learning Graybeil date: 03-06-2012 18:14 gmt revision:4 [3] [2] [1] [0] [head]

PMID-16271465 The basal ganglia: learning new tricks and loving it

  • BG analogous to the anterior forebrain pathway (AFP), which is necessary for song learning in young birds. Requires lots of practice and feedback. Studies suggest e.g. that neural activity in the AFP is correlated with song variability, and that the AFP can adjust ongoing activity in effector motor pathways.
    • LMAN = presumed homolog of cortex that receives basal ganglia outflow. Blockade of outflow from LMAN to RA creates stereotyped singing.
  • To see accurately what is happening, it's necessary to record simultaneously, or in close temporal contiguity, striatal and cortical neurons during learning.
    • Pasupathy and biller showed that changes occur in the striatum than cortex during learning.
  • She cites lots of papers -- there has been a good bit of work on this, and the theories are coming together. I should be careful not to dismiss or negatively weight things.
  • Person and Perkel [48] reports that in songbirds, the analogous GPi to thalamus pathway induces IPSPs as well as rebound spikes with highly selective timing.
  • Reference Levenesque and Parent PMID-16087877 who find elaborate column-like arrays of striatonigral terminations in the SNr, not in the dopamine-containing SNpc.

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ref: Mink-1996.11 tags: empty date: 03-05-2012 23:35 gmt revision:2 [1] [0] [head]

see {1117}

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ref: Tass-2010.02 tags: empty date: 03-05-2012 23:33 gmt revision:2 [1] [0] [head]

see {1147}

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ref: Weinberger-2009.09 tags: empty date: 03-05-2012 16:33 gmt revision:4 [3] [2] [1] [0] [head]


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ref: -0 tags: empty date: 03-03-2012 02:47 gmt revision:1 [0] [head]


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ref: -0 tags: Albin basal ganglia dopamine 1989 parkinsons huntingtons hemiballismus date: 03-02-2012 00:28 gmt revision:1 [0] [head]

PMID-2479133 The functional anatomy of basal ganglia disorders.

  • Matrix neurons mainly containing substance P mainly project upon the GPi or SNr
    • while those containing enkephalins project on the GPe.
  • Striosome neurons projecting to the SNc contain mainly substance P.
  • Classical hypothesis:
  • Hyperkinetic disorders, which are characterized by an excess of abnormal movements, are postulated to result from the selective impairment of striatal neurons projecting to the lateral globus pallidus.
    • These are suppressed by D2 receptor antagonists & exacerbated by dopamine agonists.
    • Chorea is a primary example.
    • Despite Huntingtons, traumatic, ischemic, or ablative lesions of the striatum in man or animals rarely produces chorea or atheosis (writhing movements).
    • In HD, cholinergic agonists will alleviate choreoatheosis, while anti-cholinergic drugs exacerbate it.
  • Hypokinetic disorders, such as Parkinson's disease, are hypothesized to result from a complex series of changes in the activity of striatal projection neuron subpopulations resulting in an increase in basal ganglia output.
    • opposite of HD, exacerbated by D2 antagonists and ameliorated by DA agonists, as well as anti-cholinergics.
  • Dystonia = the spontaneous assumption of unusual fixed postures lasting from seconds to minutes.

  • Standard model suggests that striatal lesions should result in spontaneous movements, while this is not the case in man or other mammals. (less inhibition on GPi / SNr -> greater susceptibility of the thalamus to competing programs (?))
  • hyperkinetic movements can be produced by infusing bicululline, a GABA receptor antagonist, into GPe -- silencing it.
  • In early HD, when chorea is most prominent, there is a selective loss of striatal neurons projecting to the LGP (enkephalin staining).
    • Substance P containing neurons are lost later in the disease.
  • Administration of D2 antagonists increases the synthesis of enkephalins and pre-proenkephalin mRNA in the striatum.
    • This presumably represents increases in neuronal activity.
    • Inhibition of GPe neurons decreases hyperkinetic movements? But STN is excitatory? This does not add up.
  • Hemiballismus may be caused by disinhibition of SNr (?) and the VA/VL/MD/CM-Pf thalamocortical projections.


  • In both PD and HD, there are both increases in the latency of initiation of saccades, slowing of saccadic velocity, and interruption of saccades.
    • In HD, there is an early loss of substance-P containing striatal terminals in the SNr, possibly resulting in over-inhibition of tectal neurons.
    • HD patients cannot supress saccades to flashed stimulus.
    • No abnormalities in saccadic control in tourette's syndrome.
  • Hikosaka: suggest that caudate neurons involved in the initiation of saccades are part of a mechanism in which sensory data are evaluated in the context of learned behaviors and anticipated actions, and then used to initiate behavior.

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ref: Timmermann-2003.01 tags: DBS double tremor oscillations DICS beamforming parkinsons date: 02-29-2012 00:39 gmt revision:4 [3] [2] [1] [0] [head]

PMID-12477707[0] The cerebral oscillatory network of parkinsonian resting tremor.

  • Patients had idiopathic unliateral tremor-dominated PD.
  • MEG + EMG -> coherence analysis. (+ DICS for deep MEG recording).
  • M1 correlated to EMG at tremor and double-tremor frequency following medication withdrawal overnight.
    • M1 leads by 15 - 25 ms, consistent with conduction delay.
  • Unlike other studies, they find that many cortical areas are also coherent / oscillating with M1, including:
    • Cingulate and supplementary motor area (CMA / SMA)
    • Lateral premotor cortex (PM).
    • SII
    • Posterior pareital cortex PPC
    • contralateral cerebellum - strongest at double frequency.
  • In contrast, the cerebellum, SMA/CMA and PM show little evidence for direct coupling with the peripheral EMG but seem to be connected with the periphery via other cerebral areas (e.g. M1)
  • Power spectral analysis of activity in all central areas indicated the strongest frequency coherence at double tremor frequency.
    • Especially cerebro-cerebro coupling.
  • These open-ended observation studies are useful!


[0] Timmermann L, Gross J, Dirks M, Volkmann J, Freund HJ, Schnitzler A, The cerebral oscillatory network of parkinsonian resting tremor.Brain 126:Pt 1, 199-212 (2003 Jan)

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ref: -0 tags: dopamine reinforcement learning funneling reduction basal ganglia striatum DBS date: 02-28-2012 01:29 gmt revision:2 [1] [0] [head]

PMID-15242667 Anatomical funneling, sparse connectivity and redundancy reduction in the neural networks of the basal ganglia

  • Major attributes of the BG:
    • Numerical reduction in the number of neurons across layers of the 'feed forward' (wrong!) network,
    • lateral inhibitory connections within the layers
    • modulatory effects of dopamine and acetylcholine.
  • Stochastic decision making task in monkeys.
  • Dopamine and ACh deliver different messages. DA much more specific.
  • Output nuclei of BG show uncorrelated activity.
    • THey see this as a means of compression -- more likely it is a training signal.
  • Striatum:
    • each striatal projection neuron receives 5300 cortico-striatal synapses; the dendritic fields of same contains 4e5 axons.
    • Say that a typical striatal neuron is spherical (?).
    • Striatal dendritic tree is very dense, whereas pallidal dendritic tree is sparse, with 4 main and 13 tips.
    • A striatal axon provides 240 synapses in the pallidum and makes 10 contacts with one pallidal neuron on average.
  • I don't necessarily disagree with the information-compression hypothesis, but I don't disagree either.
    • Learning seems a more likely hypothesis; could be that we fail to see many effects due to the transient nature of the signals, but I cannot do a thorough literature search on this.

PMID-15233923 Coincident but distinct messages of midbrain dopamine and striatal tonically active neurons.

  • Same task as above.
  • both ACh (putatively, TANs in this study) and DA neurons respond to reward related events.
  • dopamine neurons' response reflects mismatch between expectation and outcome in the positive domain
  • TANs are invariant to reward predictability.
  • TANs are synchronized; most DA neurons are not.
  • Striatum displays the densest staining in the CNS for dopamine (Lavoie et al 1989) and ACh (Holt et al 1997)
    • Depression of striatal acetylcholine can be used to treat PD (Pisani et al 2003).
    • Might be a DA/ ACh balance problem (Barbeau 1962).
  • Deficit of either DA or ACh has been shown to disrupt reward-related learning processes. (Kitabatake et al 2003, Matsumoto 1999, Knowlton et al 1996).
  • Upon reward, dopaminergic neurons increase firing rate, whereas ACh neurons pause.
  • Primates show overshoot -- for a probabalistic relative reward, they saturate anything above 0.8 probability to 1. Rats and pigeons do not show this effect (figure 2 F).

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ref: -0 tags: Romo basal ganglia movement control date: 02-24-2012 19:50 gmt revision:2 [1] [0] [head]

PMID-1483512 Role of the primate basal ganglia and frontal cortex in the internal generation of movements. I. Preparatory activity in the anterior striatum

  • Recorded from the head of the audate and rostral putamen.
  • Both spontaneous and cued / delayed-reward tasks.
  • Observed responses:
    • transient responses to cue, (2x as many to 'go' as 'nogo' cues)
    • sustained activity preceding the trigger stimulus or movement onset
      • Often this was ramp-like, indicating some sort of preparatory activity.
      • This could last 2-35 seconds, depending on the task, with a maximum of 80 s.
  • Premovement activity began 0.5-5.0s before movement onset (median 1 second).
    • Unrelated to saccadic eye movements.
    • 2/3 of these neurons were active only in spontaneous movements, and not in cued movements.
    • This is similar to activity in the frontal cortex; hence both are involved in preparing actions.

PMID-1483513 Role of primate basal ganglia and frontal cortex in the internal generation of movements. II. Movement-related activity in the anterior striatum.

  • Same experiments and recordings as above.
  • Time-locked responses to trigger, 60ms latency, independent of modality.
  • 44 neurons increased their activity before earlier EMG
  • 55 were activated with the movement,
  • 50 neurons were activated after movement onset.
  • I'm not entirely sure how this is different from above. (?)

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ref: Boulet-2006.1 tags: hemiballismus PD parkinsons STN subtalamic DBS dyskinesia rats 2006 glutamate date: 02-22-2012 18:58 gmt revision:1 [0] [head]

PMID-17050715 Subthalamic Stimulation-Induced Forelimb Dyskinesias Are Linked to an Increase in Glutamate Levels in the Substantia Nigra Pars Reticulata

  • STN-HFS-induced forelimb dyskinesia was blocked by microinjection of the Glu receptor antagonist kynurenate into the SNr and facilitated by microinjection of a mixture of the Glu receptor agonists AMPA and NMDA into the SNr.
    • Well, that just makes sense. STN is excitatory, GPi is an output structure of the BG, and stimulation should activate the area.

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ref: Steigerwald-2008.11 tags: parkinsons disease essential tremor DBS STN VIM date: 02-22-2012 18:40 gmt revision:4 [3] [2] [1] [0] [head]

PMID-18701754[0] Neuronal activity of the human subthalamic nucleus in the parkinsonian and nonparkinsonian state

  • Recorded from the STN in both PD and ET patients -- with the ET patients acting as a control (sorta; as good as we'll get).
  • ET is common neuromotor condition that involves intention tremor and movement overshoot; progresses over many years.
    • Malfunction of the olivocerebellar pathways.
    • no involvement of Dopamine-dependent pathways.
  • 65 PD patients!
  • Classified single units based on ISI & the asymmetry index, the ratio of the mode to the mean of the ISI histogram.
    • bursting or burstlike firing, intermitten grouped firing separated by periods of pauses.
      • Further analyzed for burstlike features via 'burst surprise method' Salcman 1985).
    • irregular, broad ISI CV > 85.
    • Regular tonic firing, bell shaped ISI, CV < 90.
  • PD patients had more burst-like neurons; ET patients had more irregular neurons.
  • Neurons with theta and beta characteristics predominated in bursting neurons (71/81); gamma oscillationgs were commonly found in nonbursting cells (8/11).
  • Only found synchronized beta activity in SUAs recorded from PD patients.
  • Levy: emphasized the importance of tremor for beta-band oscillations because the majority of synchronous cells were recorded from five patients with resting tremor in the operating room, whereas no synchronous pairs were found in nontremulous patients.
  • aha! a limitation of our study, however, is the lack of tremor recordings during surgeries // we were therefore not able to determine the amount of tremor-locked activity within the 3-10 Hz or transient changes in response to intermittent tremor.
    • Another limitation: no movements, attention could have wandered.
  • Still, STN firing rate increased, as per MPTP model.
  • Shift toward bursting type activity in PD.
  • Did not find differences in the proportion of neurons exhibiting intrinsic oscillatory activity or interneuronal synchronization.
  • Large proportion of neurons exhibiting theta-band activity around 4Hz in PD patients; c.f. monkeys, 10 Hz activity dominates.
    • Tremor is not an accurate reporter of pathology.


[0] Steigerwald F, Pötter M, Herzog J, Pinsker M, Kopper F, Mehdorn H, Deuschl G, Volkmann J, Neuronal activity of the human subthalamic nucleus in the parkinsonian and nonparkinsonian state.J Neurophysiol 100:5, 2515-24 (2008 Nov)

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ref: RodriguezOroz-2001.09 tags: STN SNr parkinsons disease single unit recording spain 2001 tremor oscillations DBS somatotopy organization date: 02-22-2012 18:24 gmt revision:12 [11] [10] [9] [8] [7] [6] [head]

PMID-11522580[0] The subthalamic nucleus in Parkinson's disease: somatotopic organization and physiological characteristics

  • Looks like they discovered exactly what we have discovered ... only in 2001. This is both good and bad.
    • From the abstract: "Neurones responding to movement were of the irregular or tonic type, and were found in the dorsolateral region of the STN. Neurones with oscillatory and low frequency activity did not respond to movement and were in the ventral one-third of the nucleus. Thirty-eight tremor-related neurones were recorded."
  • Again, from the abstract: "The findings of this study indicate that the somatotopic arrangement and electrophysiological features of the STN in Parkinson's disease patients are similar to those found in monkeys."
  • It may be that we want to test differential modulation / oscillation: look for differences between rest and activity, if there is sufficient support for both these in the files we have.
  • These people were much, much more careful about localization of their single-electrode tracks. E.g. they calculated electrode location relative the DBS electrode stereotatically, and referenced this to the postoperative MRI location of the treatment electrode.
  • Many more (32% of 350 neurons) responded to active or passive movement.
  • Of this same set, 15% (31 neurons) had a firing rate with rhythmical activity; 38 neurons had rhythmic activity associated with oscillatory EMG, but most of these were responsive to passive stimulation.
  • Autocorrelation of the neuronal bursting and tremor peaked at mean 7Hz, while autocorr. of EMG peaked at mean 5Hz.
  • This whole paragraph is highly interesting: ''The neuronal response associated with active movements was studied by simultaneous recording of neuronal EMG activity of the limbs. Five tremor-related neurons, recorded while a voluntary movement was performed, were available for analysis. Voluntary activation of a particular limb segment arrested the tremor. This was associated with a change in the discharges of the recorded neuron, which fired at a slower rate and in synchrony with the voluntary movement. On occasions, freezing of the voluntary movement ensued and tremor reappeared, changing the neuronal activity back to the typical 4-5Hz tremor-related activity. The cross-correlation analysis of two such neurons showed a peak frequency of 4.63 and 4.88 Hz for tremor-related activity, and 1.5 to 1.38 Hz during voluntary movement. Whether neuronal discharges in the STN preceded or followed EMG activity of the limbs could not be precisely established under the present conditions.
  • Somatotopic representation in the STN is expected from normal and MTPT-treated monkeys. Indeed, somatotopy is enhanced int he GPm of MTPT-treated monkeys.
    • This somatotopy is likely to result from organized afferent from the primary motor cortex (M1) to dorsolateral STN; this is the target of DBS treatment. Ventral and medial STN seems to project to associative and limbic cortical regions.
    • It seems they think the STN is generally not diseased, it is just a useful target for stimulating without evoked movement as in M1. This is consistent with optogenetic studies by Deisseroth [1].
    • Supporting this: "DBS of STN in Parkinson's disease improves executive motor functions, but aggravates conditional associative learning.
  • Interesting: In Parkinson's disease patients with tremor, Levy and colleagues found synchronization and a high firing rate (>10Hz) while recording pairs of neurons >600um apart.
  • Recordings of cortical activity through EEG and STN LFP showed significant coherence in the beta and gamma frequency bands during movement - consistent with corticosubthalamic motor projection. ... and suggest that the STN neurons involved in parkinsonian tremor are the same as the ones ativated during the performance of a voluntary movement. (! -- I agree with this.)
  • More: The reciprocal inhibitory-excitatory connections tightly linking the GPe and the STN may generate self-perpetuating oscillations.

Old notes:

  • this paper concentrates on STN electrophysiology in PD.
    • has a rather excellent list of references.
  • found a somatotopic organization in the STN, with most motor-related units more irregular and in the dorsolateral STN.
  • found a substantial fraction of tremor-synchronized neurons.
  • conclude that the somatotopic organization is about the same as in monkeys (?) (!)
  • M1 projects to STN, as verified through anterograde tracing studies. [1] These neurons increase their firing rate in response to passive movements.
  • there appears to be a relatively-complete representation of the body in the dorsolateral STN.


[0] Rodriguez-Oroz MC, Rodriguez M, Guridi J, Mewes K, Chockkman V, Vitek J, DeLong MR, Obeso JA, The subthalamic nucleus in Parkinson's disease: somatotopic organization and physiological characteristics.Brain 124:Pt 9, 1777-90 (2001 Sep)
[1] Gradinaru V, Mogri M, Thompson KR, Henderson JM, Deisseroth K, Optical deconstruction of parkinsonian neural circuitry.Science 324:5925, 354-9 (2009 Apr 17)

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ref: Heimer-2006.01 tags: STN DBS synchrony basal ganglia reinforcement learning beta date: 02-22-2012 17:07 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-17017503[0] Synchronizing activity of basal ganglia and pathophysiology of Parkinson's disease.

  • They worry that increased synchrony may be an epi-phenomena of tremor or independent oscillations with similar frequency.
  • Modeling using actor/critic models of the BG.
  • Dopamine depletion, as in PD, resultis in correlated pallidal activity, and reduced information capacity.
  • Other studies have found that DBS desynchronizes activity -- [1] or [2].
  • Biochemical and metabolic studies show that GPe activity does not change in Parkinsonism.
  • Pallidal neurons in normal monkeys do not show correlated discharge (Raz et al 2000, Bar-Gad et al 2003a).
  • Reinforcement driven dimensionality reduction (RDDR) (Bar-Gad et al 2003b).
  • DA activity, through action on D1 and D2 receptors on the 2 different types of MSN, affects the temporal difference learning scheme in which DA represents the difference between expectation and reality.
    • These neurons have a static 5-10 Hz firing rate, which can be modulated up or down. (Morris et al 2004).
  • "The model suggests that the chronic dopamine depletion in the striatum of PD patients is perceived as encoding a continuous state where reality is worse than predictions." Interesting theory.
    • Alternately, abnormal DA replacement leads to random organization of the cortico-striatal network, eventually leading to dyskinesia.
  • Recent human studies have found oscillatory neuronal correlation only in tremulous patients and raised the hypothesis that increased neuronal synchronization in parkinsonism is an epi-phenomenon of the tremor of independent oscillators with the same frequency (Levy et al 2000).
    • Hum. might be.
  • In rhesus and green monkey PD models, a major fraction of the primate pallidal cells develop both oscillatory and non-oscillatory pair-wise correlation
  • Our theoretical analysis of coherence functions revealed that small changes between oscillation frequencies results in non-significant coherence in recording sessions longer than 10 minutes.
  • Their theory: current DBS methods overcome this probably by imposing a null spatio-temporal firing in the basal ganglia enabling the thalamo-cortical circuits to ignore and compensate for the problematic BG".


[0] Heimer G, Rivlin M, Israel Z, Bergman H, Synchronizing activity of basal ganglia and pathophysiology of Parkinson's disease.J Neural Transm Suppl no Volume :70, 17-20 (2006)
[1] Kühn AA, Williams D, Kupsch A, Limousin P, Hariz M, Schneider GH, Yarrow K, Brown P, Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance.Brain 127:Pt 4, 735-46 (2004 Apr)
[2] Goldberg JA, Boraud T, Maraton S, Haber SN, Vaadia E, Bergman H, Enhanced synchrony among primary motor cortex neurons in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine primate model of Parkinson's disease.J Neurosci 22:11, 4639-53 (2002 Jun 1)

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ref: Guridi-2001.01 tags: STN DBS 2001 parkinsons hemiballismus Obeso date: 02-22-2012 15:14 gmt revision:8 [7] [6] [5] [4] [3] [2] [head]

PMID-11133783[0] The subthalamic nucleus, hemiballismus and Parkinson's disease: reappraisal of a neurosurgical dogma

  • Lesions of the globus pallidus, thalamus, as well as the STN can lead to hemiballismus
  • none-the-less, hemiballismus is a rather rare complication in STN DBS or lesion
  • GABA projection to the GPi is reduced in PD due to dopamine depletion
    • STN has projects glutamergic projections to GPi, so lesion would tend to worsten activity
    • STN also projects to the GPe, and lesioning it reduces hyper-activity there.
    • Therefore the balance of lesioning is to permit movements but not hemiballismus.
  • STN lesion in normal patients induces hemibalismus and chorea, but threshold for movements are raised with chronic dopamine depletion. cf {207}
  • Quality of life issues: perhaps everything has been learned already. {1124}


[0] Guridi J, Obeso JA, The subthalamic nucleus, hemiballismus and Parkinson's disease: reappraisal of a neurosurgical dogma.Brain 124:Pt 1, 5-19 (2001 Jan)

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ref: Hamani-2004.01 tags: STN subthalamic nucleus movement disorders PD parkinsons basal_ganglia globus_pallidus anatomy DBS date: 02-22-2012 15:03 gmt revision:8 [7] [6] [5] [4] [3] [2] [head]

PMID-14607789[0] The subthalamic nucleus in the context of movement disorders

  • this is a good anatomy article, very descriptive -- almost too much information to grapple with.
  • STN = important structure for the modulation of activity of basal ganglia structures
  • STN is anterior-adjacent to the red nucleus
  • The average number of neurons in each STN nucleus varies from species to species and has been estimated to be ~25 000 in rats, 35 000 in marmosets, 155 000 in macaques, 230 000 in baboons and 560 000 in humans
  • The volume of the STN is ~0.8 mm3 in rats, 2.7 mm3 in marmosets, 34 mm3 in macaques, 50 mm3 in baboons and 240 mm3 in humans.
    • Number of neurons does not scale with volume, uncertain why not.
  • STN is divided into three functional units: motor, associative, and limbic cortical regions innervate, respectively motor, associative, and limbic regions of the striatum, pallidium SNr.
    • they give a complete list of these 3 in 'intrinsic organization of the STN'
    • STN is divided into 2 rostral thirds and one cauldal third.
      • medial rostral = limbic and associative
      • lateral rostral = associative
      • dorsal = motor circuits. (the largest part, see figure 2)
        • hence, the anterodorsal is thought to be the most effective target for DBS.
  • STN is populated primarily by projection neurons
  • the dendritic field of a single STN neurons can cover up to one-half of the nucleus of rodents
  • efferent projections (per neuron, branched axons)
    • GPe, GPi, SNr 21.3%
    • GPe and SNr 2.7%
      • in both segments of the pallidum, projections are uniformly arborized & affect an extensive number of cells.
    • GPe and GPi 48%
    • GPe only 10.7%
    • 17.3% remaining toward the striatum
  • most of the cortical afferents to the STN arise from the primary motor cortex, supplementary motor area, pre-SMA, and PMd and PMv; these target the dorsal aspects of the STN.
    • afferents consist of collaterals from the pyramidal tract (layer 5) & cortical fibers that also innervate the striatum (latter more prevalent). afferents are glutamergic.
  • ventromedial STN recieves afferents from the FEF (area 8) and suppl.FEF (9)
  • GPe projects extensively to STN with GABA. see figure 3 [1]
    • almost every cell in the STN resonds to pallidal GABAergic stimulation.
    • 13.2% of GPe neurons project to GPi, STN, and SNr
    • 18.4% to GPI and STN,
    • 52.6% to only the STN and SNr
    • 15.8% remaining to the striatum.
  • DA afferents from the SNc
  • ACh from the tegmentum
  • Glutamergic afferents from the centromedian thalamus (CM)
  • Serotonin from the raphe nucleus
  • fibers from the tegmentum, SNc, motor cortex, VM.pf of the thalamus, and dorsal raphe synapse on distal dendrites
    • pallidal inhibitory fibers innervate mostly proximal dendrites and soma.
firing properties:
  • about half of STN neurons fire irregularly, 15-25% regularly, 15-50% burst.
    • bursting is related to a hyperpolarization of the cell.
  • movement-related neurons are in the dorsal portion of STN and are activated by either/both active/passive movements of single contralateral joints
  • there is a somatotopic organizaton, but it is loose.
  • many units are responsive to eye fixation, saccadic movements, or visual stim. these are in the ventral portion.
    • activation of the STN drives SNr activity, which inhibits the superior colliculus, allowing maintainance of eye position on an object of interest.
  • ahh fuck: if high currents are delivered to STN or high concentrations of GABAergic antagonists are applied abnormal movements such as dyskinesias can be elicited
    • low concentrationns of GABA antagonists induces postural asymmetry and abnormal movements, but no excessive locomotion.
  • dyskinesias result from high-frequency or high-current stimulation to the STN! low frequency stimulation induces no behavioral effects. [2]
  • small (<4% !!) lesions cause focal dystonias
  • in parkinsonian patients, activity in the STN is characterized by increased synchrony and loss of specificity in receptive fields + mildly increased mean firing rate.
    • 55% of STN units in PD patients respond to passive movements, and 24% to ipsilateral movements (really?) - indicative of the increase in receptive field size caused by the disease.


[0] Hamani C, Saint-Cyr JA, Fraser J, Kaplitt M, Lozano AM, The subthalamic nucleus in the context of movement disorders.Brain 127:Pt 1, 4-20 (2004 Jan)
[1] Sato F, Lavallée P, Lévesque M, Parent A, Single-axon tracing study of neurons of the external segment of the globus pallidus in primate.J Comp Neurol 417:1, 17-31 (2000 Jan 31)
[2] Beurrier C, Bezard E, Bioulac B, Gross C, Subthalamic stimulation elicits hemiballismus in normal monkey.Neuroreport 8:7, 1625-9 (1997 May 6)

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ref: -0 tags: DBS parkinsons stem cell therapy date: 02-21-2012 20:56 gmt revision:1 [0] [head]

PMID-21875864 Dopamine cell transplantation in Parkinson's disease: challenge and perspective.

  • Long-term strategy is to graft fetal mesencelphatic tissue into the striatum (putamen) of PD patients.
  • Requires one to four donors per side.
  • Requires immune suppression for graft survival.
  • PET offers a sensitive mechanism for assessing the success of the transplant
  • Earlier review [5].
  • Full double-blind study [2].
    • significant improvement in 39 patients, but only in the younger patients (<60 years).
    • No changes in cognitive function or personality traits.
    • Looks safe at least.
  • Second study [3]
    • Full immune suppressant action, 34 PD patients.
    • No overall clinical effect, even though there was significant FDOPA uptake.
    • significant improvement in less severely affected patients out to 2 years post-surgery.
  • Imaging results not so dramatic?
  • But off-state UPDRS significant
  • 5 of the 33 patients developed graft-induced dyskinesia in addition to therepeutic effects on akinesia -- possibly due to the lack of projecting axons?
    • FDOPA uptake was higher in the dyskinetic graft recipients with dyskinesias.
  • In second trial, 13 of the 23 implanted patients exhibited dyskinesias, but there was no difference in regional or global FDOPA uptake.
    • Might be caused by inflammatory response to the grafts, as per animal studies.
    • Another study showed the worsening if dyskinesias following withdrawal of immunosuppression.
    • Might also be related to DA exposure prior the surgery, or serotonin expression within the grafts (not controlled).
      • Suggest fluorescent cell sorting.
  • Possible solution to the donor problem: human retinal pigment endothelial cells, which produce L-DOPA naturally as an neuromelanin precursor (!!).
    • Open-label study worked, but placebo-controlled did not meet clinical significance.
  • Alternate strategy is to reprogram host somatic cells (e.g. fibroblasts), limiting the need for chronic immune suppressants.
    • Trial in one young patient with PD.

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ref: -0 tags: stem cell therapy parkinsons disease DBS date: 02-21-2012 19:04 gmt revision:1 [0] [head]

PMID-15272269 Stem cell therapy for human neurodegenerative disorders-how to make it work.

  • Before clinical trials are initiated, we need to know how to control stem cell proliferation and differentiation into specific phenotypes, induce integration into existing circuits and optimize functional recovery in animal models. (from abstract)
  • It may seem untralistic, though, to induce functional recovery by replacing cells lost through disease, considering the complexity of human brain structure and function.
    • Animal models have shown at least that it is possible.
  • Intrastriatal transplantation of human fetal mesencephatic tissue have provided a proof of principle that neuronal replacement can work in humans; neurons survive, even as the patients own SN neurons die, for up to 10 years [1,2]. Seems they can become functionally integrated into the brain, and releive symptoms of akinesia [3].
  • Sham-controlled surgieries showed modest benefit, showing that the transplantation techniques are suboptimal.
  • Dyskinesias are a common side-effect in 7-15% of patients, likely due to patchy reinervation or inflammatory response to the grafted cells.
  • Unlikely that this will be a common treatment, due to unavailabiltiy of the fetal tissue.
    • Better bet: culture the cells in vitro.
  • Requirements for graft:
    • Cells should release a regulated amount of DA
    • Cells must reverse PD in animal models
    • at least 1e5 cells must survive in humans
    • grafted cells should establish a dense terminal network throughout the striatum
    • and cells should become functionally integrated into the BG.
      • Debilitating symptoms in PD and related disorders are caused by pathological canges in non-dopaminergic systems (neuroplasticity hypothesis).
      • For more complete reversal of Parkinson's symptoms, it may be necessary to stimulate regrowth of axons from grafts in the SNpc to the striatum, which would require modification of host migration markers / growth inhibitory mechanisms [33].
  • Only embryonic stem cells have been shown to work; stem cells from the adult brain don't.
    • Human ESC may have chromosomal instability.
  • Only 5-10% of cells in fetal mesencelphatic grafts are dopaminergic neurons. It is not yet known whether it is favorable to implant pure DA cells or if the grant should contain other cells, like glia, specifically atrocytes, which control cell fate [18,19].
  • Many different pathways to dopmaninergic ESC.
    • FACS = fluorescence-assisted cell sorting.
  • To date, improvements after fetal grafts have not exceeded those found with deep brain stimulation [4,6,7], and there is no convincing evidence for the reversal of drug-resistant symptoms [4]
    • Even in animals with good reinnervation improvements are only partial [27].
  • Some evidence for the generation of striatal neurons in mice after a stroke -- figure 3.
  • Implantation of mouse ESCs into rat striatum caused teratomas in 20% of the animals [36].
    • ESCs are more likely to generate tumors when implanted in the same species that they were derived from.


  • No notable regeneration int eh cerebral cortex.
  • Targeted apoptosis of neurons in mice, leaving tissue intact, leads to reformation of cortical neurons which extend axons into the thalamus. Therefore restricted self-repair is probably due to lack of cues to trigger neurogenesis from SC.


  • Several promising lines of research, but much more basic science needs to be done regarding differentiation and delivery before treatment can be attempted.
  • Protecting existing neurons from degeneration seems like a better strategy.


  • Much more work is required, especially the basic science of differentiation / cell survival, but it's undoubtedly worth it.

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ref: Deuschl-2000.01 tags: essential tremor DBS date: 02-02-2012 03:02 gmt revision:4 [3] [2] [1] [0] [head]

PMID-10854347[0] The pathophysiology of essential tremor

  • http://www.medscape.com/viewarticle/461397_7 (you need to register to read more than 1 page)
    • Not on Neurology website :-(
  • Elevated blood concentrations of harmane and harmine were noted in essential tremor cases compared with non-essential tremor controls [30]
    • Suggest that this may be dietary or endogenous synthesis.
  • 50% hereditary.
  • Tremor is severe and debilitating; use of hands may become dangerous, eating difficult.
  • Unilateral thaladectomy is recommend in some cases; targets VIM, as does DBS.
  • Generally higher frequency than PD (resting) tremor.
    • Tremor can be Postural, Kinetic or Isometric; first two associated with ET. (or lithium cerebellar damage)


[0] Deuschl G, Elble RJ, The pathophysiology of essential tremor.Neurology 54:11 Suppl 4, S14-20 (2000)

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ref: Lenz-2002.04 tags: DBS VIM VOP thalamus essential tremor date: 02-02-2012 03:02 gmt revision:2 [1] [0] [head]

PMID-11929926[0] Single-neuron analysis of human thalamus in patients with intention tremor and other clinical signs of cerebellar disease.

  • VIM (ventral intermediate) is a cerebellar relay nucleus; VOP (ventralis oral posterior) is a pallidal relay.
  • Used pain controls. clever.
  • Observations:
    • VIM cells have a phase lag to EMG.
    • VIM firing rate decreased relative to pain controls.
    • ET patients show intention tremor -- usually under visual guidance.
      • This leads them to think that cells have been de-afferented by cerebellar injury, e.g. they get their input from basal ganglia / motor cortex / visual feedback, which has less forward phase margin (not a smith predictor), hence oscillations.


[0] Lenz FA, Jaeger CJ, Seike MS, Lin YC, Reich SG, Single-neuron analysis of human thalamus in patients with intention tremor and other clinical signs of cerebellar disease.J Neurophysiol 87:4, 2084-94 (2002 Apr)

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ref: LehAc)ricy-2001.09 tags: DBS thalamus lesions movement disorder VIM VOP date: 02-02-2012 03:02 gmt revision:2 [1] [0] [head]

PMID-11571334[0] Clinical characteristics and topography of lesions in movement disorders due to thalamic lesions

  • So hard to find a good sagittal diagram of the human thalamus!


[0] Lehéricy S, Grand S, Pollak P, Poupon F, Le Bas JF, Limousin P, Jedynak P, Marsault C, Agid Y, Vidailhet M, Clinical characteristics and topography of lesions in movement disorders due to thalamic lesions.Neurology 57:6, 1055-66 (2001 Sep 25)

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ref: Zaghloul-2009.03 tags: DBS STN reinforcement learning humans unexpected reward Baltuch date: 01-26-2012 18:19 gmt revision:1 [0] [head]

PMID-19286561[0] Human Substantia Nigra Neurons Encode Unexpected Financial Rewards

  • direct, concise.
  • 15 neurons in 11 patients -- we have far more!


[0] Zaghloul KA, Blanco JA, Weidemann CT, McGill K, Jaggi JL, Baltuch GH, Kahana MJ, Human substantia nigra neurons encode unexpected financial rewards.Science 323:5920, 1496-9 (2009 Mar 13)

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ref: Nishioka-2008.12 tags: STN hemiballismus lesion stroke MRI neurosurgery date: 01-26-2012 17:31 gmt revision:3 [2] [1] [0] [head]

PMID-18842415[0] Transient hemiballism caused by a small lesion of the subthalamic nucleus.

  • Hemiballism is most commonly caused by ischemic stroke and most cases have a favorable prognosis.
  • Lesions directly involving the subthalamic nucleus (STN) are the cause of a minority of cases but are usually associated with poor prognosis.
  • We report two patients with a small STN lesion who presented with transient hemiballism.
  • This may be a useful ref in the future.
  • This reports the same result: PMID-17702635


[0] Nishioka H, Taguchi T, Nanri K, Ikeda Y, Transient hemiballism caused by a small lesion of the subthalamic nucleus.J Clin Neurosci 15:12, 1416-8 (2008 Dec)

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ref: Kuhn-2004.04 tags: STN LFP syncronization movement motor planning parkinsons PD DBS beta date: 01-26-2012 17:28 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

PMID-14960502[0] Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance.

  • Asked 6 PD patients to play a game where they were warned to move / not to move.
  • Beta-frequency (20hz) power decreased prior to movement, with a time course correlated to reaction time.
    • This was followed by a late post-movement increase in beta power.
  • No-go trials showed a brief dip in beta power, with quick resumption.
  • conclude that:
    • the subthalamic nucleus is involved in the preparation of externally paced voluntary movements in humans
    • the degree of synchronization of subthalamic nucleus activity in the beta band may be an important determinant of whether motor programming and movement initiation is favored or suppressed. (hum, maybe).
  • found via Romulo's references; see the list of papers that cite it.


[0] Kühn AA, Williams D, Kupsch A, Limousin P, Hariz M, Schneider GH, Yarrow K, Brown P, Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance.Brain 127:Pt 4, 735-46 (2004 Apr)

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ref: Lee-2005.07 tags: STN subthalamic nucleus hemiballismus DBS date: 01-26-2012 17:24 gmt revision:3 [2] [1] [0] [head]

PMID-16032642[0] Common causes of hemiballism.

  • stroke of the STN results in hemiballismus - wild movements of the limbs. recall the input to the STN is inhibitory from GPe, and the output is exitatory to the GPi. chemical treatment is via dopamine blockade (1976!)
  • hemiballism is rare, but usually associated with lesion to the contralateral STN.
    • however, half the cases of hemiballismus are associated with damage to the afferent or efferent pathways to the STN.
    • diabetes type 2 also commonly causes hemiballismus (hyperglycemia in asian women!)
  • hemiballismus is absent in sleep - the thalamocortical relay must be turned off.
  • hemiballismus is generally associated with high metabolic activity in the basal ganglia.
  • does this mean that stimulation to the STN in healthy monkeys will disinhibit large, possibly conflicting movements?
  • my thought: the subthalamic nucleus must be involved in the selection and regulation of appropriate movements.


[0] Lee HS, Kim SW, Yoo IS, Chung SP, Common causes of hemiballism.Am J Emerg Med 23:4, 576-8 (2005 Jul)

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ref: Beurrier-1997.05 tags: STN stimulation hemiballismus 2007 DBS date: 01-26-2012 17:20 gmt revision:4 [3] [2] [1] [0] [head]

PMID-9189903[] Subthalamic stimulation elicits hemiballismus in normal monkey.

  • the effects of stimulation on normal waking primates has never been evaluated (doh!)
  • In the normal monkey, HFS appears reversibly to incapacitate the STN and allow the emergence of involuntary proximal displacements, due to disinhibition of the thalamo-cortical pathway
  • in MPTP-treated monkey HFS buffers STN activity and alleviates akinesia and rigitity by reducing inputs to the internal segment of the globus pallidus. (STN output is excitatory) (or so the theory at the time goes)
  • perhaps i will need to buy this article ;(


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ref: Klawans-1976.12 tags: STN DBS heminallisms date: 01-26-2012 17:17 gmt revision:3 [2] [1] [0] [head]

PMID-980081[0] Treatment and prognosis of hemiballismus.

  • Acute hemiballismus due to a cerebrovascular lesion may have a grave prognosis. In the past nine years, we have treated 11 patients who had an acute onset of hemiballismus believed to be the result of an acute vascular lesion with neuroleptic drugs (most frequently haloperidol). None of the 11 died, and the movement disorders were greatly reduced or eliminated. In eight patients the drugs were withdrawn within six months, without recurrence of the movement disorders. Spinal-fluid homovanillic acid levels were increased in three patients, suggesting that altered dopaminergic feedback mechanisms may be involved in the pathophysiology of hemiballismus. Our observations suggest that the prognosis of hemiballismus is not necessarily as grave as has been believed, and that neuroleptic therapy may alter the outcome of this disorder.


[0] Klawans HL, Moses H 3rd, Nausieda PA, Bergen D, Weiner WJ, Treatment and prognosis of hemiballismus.N Engl J Med 295:24, 1348-50 (1976 Dec 9)

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ref: Elble-1996.03 tags: tremor STN VIM thalamus basal_ganglia Elble Parkinson's ET dyskinesia thalamus VIM DBS date: 01-24-2012 21:19 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

PMID-8849968[] Central Mechanisms of Tremor -- available through Duke's Ovid system. also in email.

  • focuses at first on the nonlinear aspect of all control: the systems are hard to understand because of the complexities of their interactions.
    • nonlinear systems are capable of complex interactions that are not predicted by the sum of their individual behaviors.
  • in general, there are two different types of tremor:
    • mechanical reflex oscillations (depend on sensorimotor loops), permit damped oscillations in response to pulsate perturbations.
      • is effected by the stifness and inertia of the segment involved.
    • central oscillations
      • frequencies independent of limb mechanics/segment length.
      • still subject to modulation by sensorimotor feedback.
      • if the tremor is at the same frequency as the mechanical resonance, the tremor will be worse!
  • physiologic tremor has both components of mechanical oscillations (3-5Hz) and central oscillations (8-12hz), which are usually attenuated by the low-pass property of the musculoskeletal system.
    • associated spindle and tendon organ discharge are not sufficient to produce 8 - 12 Hz oscillation - hence, this is most likely from a central source, eg. the cortex, inferior olive, and thalamus.
  • Essential tremor is also centrally generated, though it appears to be affected by somatosensory driving.
    • essential tremor frequency is strongly correlated with patient age (where the frequency decreases with increasing age).
    • the origin of ET is unknown: postmortem examinations reveal no deficits in M1/S1, thalamus, inferior olive, raphe nucleus, and reticular nuclei, globus pallidus, and spinal cord...
    • but, the inferior olive seems to be the most likely culprit:
      • tremor induced by harmaline increased inhibition-rebound properties of neurons, and this induces intention-related tremor in monkeys
      • harmaline induced olivary oscillation is similar to ET in terms of frequency, EMG, and drug-response.
      • olivary hypothesis is supported by PET scans, which show increased glucose consumption there in ET patients.
      • the ventrolateral (VL) thalamus and Ventralis intermedius (VIM) receives input from the contralateral cerebellar nuclei.
        • this is why VIM is such a good target for treatment of ET.
  • parkinsons tremor:
    • VOP is a better target for treating bradykinesia and other symptoms of PD, while VIM is the best for treating tremor
    • neurons in the globus pallidus and STN become entrained to tremor. STN lesion / HFS is effective in treating dyskinesia and other PD symptoms.
    • in MPTP monkeys, STN/ GPi neurons are also entrained to the tremor frequency.
  • other tremor:
    • neuropathic/tumorogenic tremor usually takes weeks to appear, suggesting that CNS reorganization is a cause of tremor, not intrinsic sensorimotor deafferentation
      • local lesions in the striatum, thalamus, & globus pallidus often cause dystonias, not tremor.
  • Cerebellar tremor
    • seems to be caused by an inability to properly compensate/ brake with antagonist muscles during voluntary and postural movements. movement control becomes heavily dependent on sensory feedback, which is often too slow for adequate compensation.
  • neuroleptic drugs can often cause tremor (or tardive dyskinesia). Neurolepric - calming, tranquilizer, antipsychotic.
    • lithium can cause permanent tremor due to cerebellar gliosis!
  • VOP projects to the supplementary motor area (SMA) and dorsolateral prefrontal cortex (DLPFC) PMID-21629131 ; VIM projects to M1 & contralateral cerebellum, as mentioned above.


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ref: Parush-2011.01 tags: basal ganglia reinforcement learning hypothesis frontiers israel date: 01-24-2012 04:05 gmt revision:2 [1] [0] [head]

PMID-21603228[0] Dopaminergic Balance between Reward Maximization and Policy Complexity.

  • model complexity discounting is an implicit thing.
    • the basal ganglia aim at optimization of independent gain and cost functions. Unlike previously suggested single-variable maximization processes, this multi-dimensional optimization process leads naturally to a softmax-like behavioral policy
  • In order for this to work:
    • dopamine directly affects striatal excitability and thus provides a pseudo-temperature signal that modulates the tradeoff between gain and cost.


[0] Parush N, Tishby N, Bergman H, Dopaminergic Balance between Reward Maximization and Policy Complexity.Front Syst Neurosci 5no Issue 22 (2011)

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ref: Brown-2001.12 tags: EMG ECoG motor control human coherence dopamine oscillations date: 01-19-2012 21:41 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-11765129[0] Cortical network resonance and motor activity in humans.

  • good review.
  • No coherence between ECoG and eMG below 12 Hz; frequency coherence around 18 Hz.
    • This seen only in high-resolution ECoG; lower resolution signals blurs the sharp peak.
  • Striking narrowband frequency of coherence.
  • ECoG - ECoG coherence not at same frequency as EMG-ECoG.
  • Marked task-dependence of these coherences, e.g. for wrist extension and flexion they observed similar EMG/ECoG coherences; for different tasks using the same muscles, different patterns of coherence.
  • Pyramidal cell discharge tends to be phase-locked to oscillations in the local field potential (Murthy and Fetz 1996)
    • All synchronization must ultimately be through spikes, as LFPs are not transmitted down the spinal cord.
  • Broadband coherence is pathological // they note it occurred during cortical myclonus (box 2)
  • Superficial chattering pyramidal cells (!!) firing bursts of frequency at 20 to 80 Hz, interconnected to produce spike doublets (Jefferys 1996).
  • Dopamine restores coherence between EMG and ECoG in a PD patient.


[0] Brown P, Marsden JF, Cortical network resonance and motor activity in humans.Neuroscientist 7:6, 518-27 (2001 Dec)

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ref: BarGad-2003.12 tags: information dimensionality reduction reinforcement learning basal_ganglia RDDR SNR globus pallidus date: 01-16-2012 19:18 gmt revision:3 [2] [1] [0] [head]

PMID-15013228[] Information processing, dimensionality reduction, and reinforcement learning in the basal ganglia (2003)

  • long paper! looks like they used latex.
  • they focus on a 'new model' for the basal ganglia: reinforcement driven dimensionality reduction (RDDR)
  • in order to make sense of the system - according to them - any model must ingore huge ammounts of information about the studied areas.
  • ventral striatum = nucelus accumbens!
  • striatum is broken into two, rough, parts: ventral and dorsal
    • dorsal striatum: the caudate and putamen are a part of the
    • ventral striatum: the nucelus accumbens, medial and ventral portions of the caudate and putamen, and striatal cells of the olifactory tubercle (!) and anterior perforated substance.
  • ~90 of neurons in the striatum are medium spiny neurons
    • dendrites fill 0.5mm^3
    • cells have up and down states.
      • the states are controlled by intrinsic connections
      • project to GPe GPi & SNr (primarily), using GABA.
  • 1-2% of neurons in the striatum are tonically active neurons (TANs)
    • use acetylcholine (among others)
    • fewer spines
    • more sensitive to input
    • TANs encode information relevant to reinforcement or incentive behavior


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ref: Deco-2009.05 tags: stochastic dynamics Romo memory computation date: 01-16-2012 18:54 gmt revision:1 [0] [head]

PMID-19428958[0] Stochastic dynamics as a principle of brain function

  • Noise produces a 'probabalistic choice'.
  • Used simulated integrate and fire neurons.
  • justification: "and the taking of probabilistic decisions that on an individual trial may be non-optimal, but that may be adaptive by providing evidence about whether the probability of opportunities is changing in the world". So, a broader optimality in an uncertain world?
  • I'm skimming this, but looks like they largely are focused on frequency discrimination tasks.
  • Lots of text.


[0] Deco G, Rolls ET, Romo R, Stochastic dynamics as a principle of brain function.Prog Neurobiol 88:1, 1-16 (2009 May)

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ref: Mohseni-2005.09 tags: recording telemetry radio Najafi wireless date: 01-15-2012 22:22 gmt revision:3 [2] [1] [0] [head]

PMID-16200750[0] Wireless Multichannel Biopotential Recording Using an Integrated FM Telemetry Circuit Pedram Mohseni, Khalil Najafi, Steven Eliades, Xiaoquin Wang.


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ref: Obeid-2004.02 tags: Obeid multichannel telemetry wireless recording date: 01-15-2012 22:06 gmt revision:3 [2] [1] [0] [head]

PMID-14757342[0] A multichannel telemetry system for single unit neural recordings

  • 16 channels; only transmit 12.
  • 45 minute battery life, 4W power consumption.
  • Uses a 486 index-card sized PC running DOS.
    • TCP/IP connection from host PC to wearable computer; UDP transmission of neural data.
  • 802.11b via a WAN ethernet card
  • 235g
  • AFE see [1]
  • 100mW radiated power.
  • Latency 680us input to output.
  • Did not notice any problems due to multipath.
  • See also PMID-17945926[2] for similar work


[0] Obeid I, Nicolelis MA, Wolf PD, A multichannel telemetry system for single unit neural recordings.J Neurosci Methods 133:1-2, 33-8 (2004 Feb 15)
[1] Obeid I, Nicolelis MA, Wolf PD, A low power multichannel analog front end for portable neural signal recordings.J Neurosci Methods 133:1-2, 27-32 (2004 Feb 15)
[2] Parthasarathy J, Hogenson J, Erdman AG, Redish AD, Ziaie B, Battery-operated high-bandwidth multi-channel wireless neural recording system using 802.11b.Conf Proc IEEE Eng Med Biol Soc 1no Issue 5989-92 (2006)

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ref: Mojarradi-2003.03 tags: MEMS recording telemetry Normann Andersen wireless date: 01-15-2012 04:29 gmt revision:2 [1] [0] [head]

PMID-12797724[0] A miniaturized neuroprosthesis suitable for implantation into the brain.

  • Standard tricks: cascode configuration, deep-ohmic PMOS Devices for resistive feedback, wide PMOS weak-inversion input stage for good transconductance and low noise.
  • Varaible power for variable noise levels & bandwidths.
  • Wireless transceiver and power stage are in early concept stages.


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ref: Wyler-1979.09 tags: operant control reinforcement schedule Wyler Robbins date: 01-07-2012 22:09 gmt revision:1 [0] [head]

PMID-114271[0] Operant control of precentral neurons: the role of reinforcement schedules.

  • Tried 3 different rewarding schedules:
    • Reward when the ISI was within a window 30-60ms
    • Differential reward, +2 or +3 when ISI was 45-60ms, +1 when 30-45ms
    • Nonspecific, constant applesauce reward.
  • No change in the mode of the ISI was observed, independent of reward schedules.


[0] Wyler AR, Robbins CA, Operant control of precentral neurons: the role of reinforcement schedules.Brain Res 173:2, 341-3 (1979 Sep 14)

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ref: Olds-1967.01 tags: Olds 1967 limbic system operant conditioning recording rats electrophysiology BMI date: 01-06-2012 03:59 gmt revision:2 [1] [0] [head]

PMID-6077726[0] The limbic system and behavioral reinforcement

  • Can't seem to find Olds 1965, as was a conference proceeding .. this will have to do, despite the lack of figures. images/966_1.pdf
  • First reference I can find of chronic (several weeks) (4-9 microelectrodes, single) recording from the rat.
  • Basically modern methods: commutator + solid state preamplifiers mounted to a counterbalanced slack-relieving arm.
    • If unit responses were observed in recordings from a given probe a week after surgery they were usually recordable indefinitely. 44 years later ...
  • Used a primitive but effective analog spike discriminator based on:
    • minimum amplitude
    • maximum amplitude
    • minimum fall time
    • maximum fall time.
  • Also had a head movement artifact detector, which blanked the recordings (stopped the paper roll) for 2 sec.
  • Reinforced on 'bursting', threshold sufficiently high that it only occurred once every 5-15 minutes.
  • Food reinforcement or 1/4 second train of brain stimulation (30ua, 60Hz, sine, in hypothalamus).
  • Reinforcement was conditioned on an 'acquisition' signal, which is visual (?) Bursting is rewarded for 2 minutes, ignored for 8 minutes.
  • Also recorded control neurons.
  • (they were looking at these things as though anew!) "The most striking aspect of the records so formed [on sheets of paper] was that all discriminators at one time or another exhibited rate changes that had the appearance of waves with a period of 10 to 20 minutes. Waves between units in the same animal were to some degree synchronized." Then describes a ramp ..
  • Longer term variations: FR would vary by a factor of 2-5 over a period of several hours.
    • This would make negatively correlated neurons (on a short time scale) appear positively correlated over long time scales (have to fix this in the BMI!)
  • As this was a conditional reinforcement task, they unexpectedly found that the acquisition periods were systematically different than extinction periods
    • More like pavlovian conditioning, esp in the hippocampus, where a conditioned response was also reflected on a control neuron.
    • Even when the light was lit throughout the acquisition period was replaced by a bell at the beginning of the acq. period, there was still a sustained change in FR.
      • Then during the extinction period: it appeared from the record of responses that a definite operant behavior was tried several times and then stopped altogether."
  • In the pontine nucleus (relay from M1 to cerebellum, v. roughly), judging from the control responses, all were conditioned.
    • Pontine responses seem to correspond with movement of the eyes or head that did not set off the movement detector/blanker.
  • Saw brief and very fast bursts during the extinction periods of the kind that Evarts found to characterize pyramical neurons during sleep.
  • When units shifted from food reward to ICS reward, units became undiffarentiated, and within a day they would be reconditioned.
  • Also tried paralyzing the animal to see if it could still generate operant responses; the animal died, results inconclusive.
  • Flood lights made it hard for the rats to produce the operant behavior.


[0] Olds J, The limbic system and behavioral reinforcement.Prog Brain Res 27no Issue 144-64 (1967)

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ref: notes-0 tags: clementine operant conditioning 041707 pyramidal tract tlh24 date: 01-06-2012 03:12 gmt revision:4 [3] [2] [1] [0] [head]

It appears that operant/feedback training of one neuron (channel 29, in SMA region) works fine (not great, but fine). In the experiment performed prior to visiting Seattle, on April 10 2007, I was not convinced that the neuron was controlling anything. Now, it is apparent that the monkey has some clue as to what he is doing. Today I made a simple change: I made the filtering function sum (all spikes) 1/12 * x*(x-1)^2, where x = time - time_of_spike. In comparison to a butterworth filter, this has no rebound oscillation & makes the estimation of firing rate much more transparent. It averages over approximately 500ms ~= lowcut of 1.5hz? I see no reason to change this filtering function much, as it works fine. Spikes were binned at 100hz as input to this function, but that should be equivalent to binning at 1khz etc.

Next time, i want to do 2d, where channel 62 controls the Y-axis. really should try to determine the approximate tunings of these cells. I'm somewhat concerned as this channel seems to have a much lower mean firing rate than channel 29. According to the literature, PTNs have high firing rates and strong tuning...

for reference, here is the channel used for the one-neuron BMI, recorded April 10. It has not changed much in the last 7 days.

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ref: thesis-0 tags: clementine 051607 operant conditioning tlh24 date: 01-06-2012 03:09 gmt revision:1 [0] [head]

the cells were, basically, as usual for today. did 1-d BMI on channel 29; worked somewhat (nothing dramatic; mk is out of practice?)

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ref: thesis-0 tags: clementine Kalman wiener tlh24 date: 01-06-2012 03:08 gmt revision:3 [2] [1] [0] [head]

040507. wiener pred. same deal as {262}

kalman fit/pred.

per-unit and channel aggregate SNR summary

    unit     chan       lag       snr       behav var
    1.0000   69.0000    1.0000    1.1159    2.0000
    1.0000   58.0000    1.0000    1.1074    6.0000
    2.0000   44.0000    2.0000    1.1040    2.0000
    2.0000   44.0000    1.0000    1.0953    2.0000
    2.0000   93.0000    1.0000    1.0868    3.0000
    2.0000   64.0000         0    1.0728    3.0000
    1.0000   69.0000    2.0000    1.0698    2.0000
    1.0000   32.0000         0    1.0684    3.0000
    2.0000   44.0000         0    1.0634    8.0000
    1.0000   58.0000         0    1.0613    6.0000
    1.0000   33.0000    1.0000    1.0594    1.0000
    2.0000   93.0000    3.0000    1.0523    3.0000
    1.0000   63.0000         0    1.0507    3.0000
    1.0000   67.0000    1.0000    1.0490    5.0000
    1.0000   47.0000         0    1.0489    3.0000
    1.0000   12.0000    4.0000    1.0472    3.0000
    2.0000   93.0000    2.0000    1.0460    3.0000
    1.0000   24.0000         0    1.0459    3.0000
    1.0000   42.0000    1.0000    1.0447    6.0000
    1.0000   24.0000    1.0000    1.0440    3.0000
    1.0000   69.0000    3.0000    1.0431    2.0000
    2.0000   60.0000         0    1.0429    5.0000
    1.0000   61.0000         0    1.0410    4.0000
    1.0000   12.0000    1.0000    1.0400    1.0000
    1.0000   32.0000    3.0000    1.0395    3.0000
    1.0000    8.0000    1.0000    1.0387    1.0000
    1.0000   33.0000         0    1.0386   11.0000
    1.0000         0    1.0000    1.0383    4.0000
    2.0000   77.0000    2.0000    1.0383    1.0000
    1.0000   47.0000    1.0000    1.0382    3.0000
    2.0000   60.0000    1.0000    1.0376   10.0000
    2.0000   77.0000    1.0000    1.0375    1.0000
    1.0000   28.0000    1.0000    1.0374    1.0000
    1.0000   69.0000    5.0000    1.0359    3.0000
    1.0000   42.0000         0    1.0358    3.0000
    1.0000    8.0000         0    1.0357    3.0000
    1.0000   63.0000    3.0000    1.0357    3.0000
    2.0000   68.0000    1.0000    1.0348    1.0000
    1.0000   51.0000         0    1.0343    3.0000
    1.0000   30.0000    1.0000    1.0341    1.0000
    1.0000   24.0000    2.0000    1.0341    3.0000
    2.0000   93.0000    5.0000    1.0340    3.0000
    1.0000   63.0000    4.0000    1.0338    3.0000
    1.0000   63.0000    2.0000    1.0337    3.0000
    1.0000   12.0000    2.0000    1.0329    1.0000
    2.0000   23.0000    1.0000    1.0325    1.0000
    1.0000   46.0000    1.0000    1.0324    2.0000
    1.0000   28.0000         0    1.0323    1.0000
    2.0000   93.0000    4.0000    1.0321    3.0000
    1.0000   58.0000    3.0000    1.0316    6.0000
    1.0000   47.0000    2.0000    1.0314    6.0000
    1.0000   48.0000         0    1.0311    4.0000
    1.0000   12.0000    3.0000    1.0310    3.0000
    1.0000   12.0000         0    1.0309    3.0000
    1.0000   48.0000    1.0000    1.0303   11.0000
    1.0000   28.0000    2.0000    1.0300    1.0000
    2.0000   60.0000    2.0000    1.0294   10.0000
    1.0000   46.0000         0    1.0293    8.0000
    1.0000   49.0000         0    1.0291    3.0000
    1.0000   24.0000    3.0000    1.0286    1.0000
    2.0000   77.0000    3.0000    1.0282    3.0000
    1.0000    8.0000    2.0000    1.0282    1.0000
    2.0000   15.0000    1.0000    1.0281    3.0000
    2.0000   68.0000    2.0000    1.0278    1.0000
    2.0000   23.0000         0    1.0273    1.0000
    1.0000  112.0000    1.0000    1.0261    7.0000
    1.0000   69.0000    4.0000    1.0258    3.0000
    2.0000   92.0000    3.0000    1.0244    3.0000
    2.0000   42.0000    1.0000    1.0244   11.0000
    1.0000   58.0000    2.0000    1.0238    3.0000
    1.0000   61.0000    1.0000    1.0234    7.0000
    1.0000   32.0000    4.0000    1.0232    3.0000
    1.0000   33.0000    2.0000    1.0231    1.0000
    1.0000   30.0000    4.0000    1.0231    3.0000
    1.0000   46.0000    2.0000    1.0227    2.0000
    1.0000   30.0000    3.0000    1.0226    3.0000
    1.0000   45.0000         0    1.0225    3.0000
    1.0000   60.0000         0    1.0225    3.0000
    2.0000   84.0000    5.0000    1.0222    3.0000
    1.0000   32.0000    1.0000    1.0221    1.0000
    1.0000   24.0000    4.0000    1.0220    1.0000
    1.0000   28.0000    3.0000    1.0219    1.0000
    1.0000   64.0000    1.0000    1.0216    4.0000
    2.0000   84.0000    1.0000    1.0215    3.0000
    1.0000   30.0000         0    1.0212    3.0000
    2.0000   77.0000    5.0000    1.0211    3.0000
    1.0000   63.0000    1.0000    1.0210    3.0000
    1.0000   33.0000    4.0000    1.0209    1.0000
    1.0000    7.0000    1.0000    1.0209    3.0000
    2.0000   35.0000         0    1.0202    3.0000

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ref: thesis-0 tags: clementine 042007 operant conditioning biofeedback tlh24 date: 01-06-2012 03:08 gmt revision:4 [3] [2] [1] [0] [head]

channel 29 controlled the X direction:

channel 81, the Y direction (this one was very highly modulated, and the monkey could get to a high rate ~60Hz. note that both units are sorted as one -- I ought to do the same on the other channels from now on, as this was rather predictive (this is duplicating Debbie Won's results):

However, when I ran a wiener filter on the binned spike rates (this is not the rates as estimated through the polynomial filter), ch 81 was most predictive for target X position; ch 29, Y target position (?). This is in agreement with population-wide predictions of target position: target X was predicted with low fidelity (1.12; cc = 0.35 or so); target Y was, apparently, unpredicted. I don't understand why this is, as I trained the monkey for 1/2 hour on just the opposite. Actually this is because the targets were not in a random sequence - they were in a CCW sequence, hence the neuronal activity was correlated to the last target, hence ch 81 to target X!

for reference, here is the ouput of bmi_sql:

order of columns: unit,channel, lag, snr, variable

ans =

    1.0000   80.0000    5.0000    1.0909    7.0000
    1.0000   80.0000    4.0000    1.0705    7.0000
    1.0000   80.0000    3.0000    1.0575    7.0000
    1.0000   80.0000    2.0000    1.0485    7.0000
    1.0000   80.0000    1.0000    1.0402    7.0000
    1.0000   28.0000    4.0000    1.0318    8.0000
    1.0000   76.0000    2.0000    1.0238   11.0000
    1.0000   76.0000    5.0000    1.0225   11.0000
    1.0000   17.0000         0    1.0209   11.0000
    1.0000   63.0000    3.0000    1.0202    8.0000

movies of the performance are here:

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ref: thesis-0 tags: clementine 042107 operant conditioning tlh24 date: 01-06-2012 03:08 gmt revision:5 [4] [3] [2] [1] [0] [head]

I tried to train Clem, once again, to do 2d BMI, this time with channel 69 for X and channel 71 for Y. X worked rather well, to a point - he realized that he could control it with left shoulder contractions, and did so (did not get a video of this). I did, however, get a video of the game, which is here:

Y training/performance was abysmal and hence did not try 2D control. Channel 71 would become silent whenever he began to pay attention; I'm not sure why. It would fire vigorously when he turned around and rested; the unit had a high firing rate at rest. I did not get a pic of the sortclient for today, but ch 29 was there as usual (though i did not use it) & channel 71 had the characteristic sharp V shape; perhaps it was an interneuron?? I don't know.

anyway, the data is in SQL on hardm.ath.cx. (the real proof is in the pudding, of course).

we really need to put the BMI game in his home cage, so motivation is not such a large issue

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ref: neuro notes-0 tags: clementine thesis electrophysiology fit predictions tlh24 date: 01-06-2012 03:07 gmt revision:4 [3] [2] [1] [0] [head]

ok, so i fit all timestamps from clem022007001 & timarm_log_070220_173947_k.mat to clementine's behavior, and got relatively low SNR for almost everything - despite the fact that I am most likely overfitting. (bin size = 7802 x 1491) the offset is calibrated @ 2587 ms + 50 to center the juice artifact in the first bin. There are 10 lags. There are 21 sorted units.

same thing, but with only the sorted units. juice prediction is, of course, worse.

now, for file clem022007002 & timarm_log_070220_175636_k.mat. first the unsorted:

and the sorted:

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ref: research-0 tags: clementine tlh24 Kalman thesis date: 01-06-2012 03:07 gmt revision:3 [2] [1] [0] [head]

clementine, 040207, Miguel's sorting. top 200 lags selected via bmisql.m , decent SNR on all channels but I had to z-score the state and measurement matricies.

-- standard wiener

-- linear kalman.

-- associated behavior

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ref: notes-2007 tags: clementine BMI robot kinarm timarm 032807 date: 01-06-2012 00:07 gmt revision:14 [13] [12] [11] [10] [9] [8] [head]

  1. http://m8ta.com/tim/clementine.MOV -- opens with totem, MJPG compressor.
  2. http://m8ta.com/tim/timarm_servocontroller.JPG
  3. http://m8ta.com/tim/images/spikeInformation_shuffled.jpg
    1. shuffled information distribution -- high significance level ;)
  4. kinarm.
    1. http://www.hardcarve.com/tim/kinarm.JPG
    2. http://www.hardcarve.com/tim/kinarm2.JPG
    3. http://www.hardcarve.com/tim/kinarm3.JPG
  5. robot svg or timarm png
    1. http://www.hardcarve.com/tim/timarm/timarm_side.jpg
    2. http://m8ta.com/tim/robotPulleyDetail.png
  6. bmi predictions clem 032807
      1. x & y predictions
      1. x & y predictions
      1. z velocity predictions - pretty darn good, snr 2
    1. Movie of the day: http://m8ta.com/tim/clem032807_3dBMI.MPG
      1. cells for that day - 40 in all

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ref: BASMAJIAN-1963.08 tags: original BMI M1 human EMG tuning operant control Basmajian date: 01-05-2012 00:49 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-13969854[0] Control and Training of Individual Motor Units

  • humans have the ability to control the firing rate of peripheral motor units with a high resolution.
  • "The quality of control over anterior horn cells may determine the rates of learning" yup!
  • "Some learn such esquisite control that they soon can produce rhythms of contraction in one unit, imitating drum rolls etc"
  • the youngest persons were among both the best and worst learners.
  • after about 30 minutes the subject was required to learn how to repress the first unit and to recruit another one.
    • motor unit = anterior horn cell, its axon, and all the muscle fibers on which the terminal branches of the axon end. max rate ~= 50hz.
    • motor units can be discriminated, much like cortical neurons, by their shape.
    • some patients could recruit 3-5 units altogether - from one bipolar electrode!
      • in playback mode (task: trigger the queried unit), several subjects had particular difficulty in recruiting the asked-for units. "They groped around in their conscious efforts to find them sometimes, it seemed, only succeded by accident"
    • some patients could recruit motor units in the absence of feedback, but they were unable to explain how they do it.
  • 0.025 (25um) nylon-insulated Karma alloy EMG recording wire.
  • feedback: auditory & visual (oscilloscope).
  • motor units have a maximum rate, above which overflow takes place and other units are recruited (in accord with the size principle).
  • "The controls (are) learned so quickly, are so esquisite, are so well retained after the feedbacks are eliminated that one must not dismiss them as tricks"


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ref: Fetz-2000.12 tags: motor control spinal neurons interneurons movement primitives Fetz review tuning date: 01-03-2012 23:08 gmt revision:4 [3] [2] [1] [0] [head]

PMID-11240278[0] Functions of mammalian spinal interneurons during movement

  • this issue of current opinion in neuro has many reviews of motor control
  • points out that the Bizzi results (they microstimulated & observed a force-field-primitive type organization)
    • others have found that this may be a consequence of decerebration + the structure of the biomechanical groupings of muscles. (see 'update').
  • intraspinal electrodes in the cat provide a secure and reliable method of eliciting forces and movements.
  • CM (corticomotor) cells more often represent synergistic groups of muscles, whereas premotor spinal interneurons are organized to target specific muscles.
    • CMs are therefore more strictly recruited for particular movements.
  • interneurons (IN) are, of course, arrayed in such a way so that antagonist and agonist muscles cross-inhibit eachother (for efficiency)
    • however, we are still able to control the endpoint impedance of the arm - how?
  • spinal interneurons modulate activity during wait period prior to movement!
    • there might be substantial interaction between the cortex and spinal cord.. subjects asked to imagine pressing a foot pedal showed enhanced reflexes in the involved soleus muscle.
      • cognitive priming?
  • spinal reflexes are strongly modulated in movement.


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ref: bookmarks-0 tags: EMG schematic amplifier prosthetic myopen date: 01-03-2012 23:08 gmt revision:3 [2] [1] [0] [head]

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ref: Sodagar-2007.06 tags: neural recording telemetry Wise Najafi mulitichannel electrophysiology Michigan ASIC date: 01-03-2012 23:07 gmt revision:4 [3] [2] [1] [0] [head]

PMID-17554826[0] A fully integrated mixed-signal neural processor for implantable multichannel cortical recording.

  • document is rich in details! looks pretty well designed, too.
  • Michigan 3-d electrodes
  • inductively powered, 2Mbps output
  • 64 channels
  • 18b/spike for 64 channels in scan mode, continuous waveforms on 2 channels in monitor mode
  • programmable analog spike detection. resolution: 5 bits.
  • no timestamps - send them out as they come in, with a clock rate fast enough so that this does not matter.
    • temporary storage in SRAM
    • time compression and buffering is somewhat complex (?)
  • only transmit threshold crossings, positive, negative, and both.
    • they do not detail how the signal is telemetered - perhaps this is for another publication.
  • fabricated chip occupies 3.5 x 2.7 mm. 0.5um process.
  • fabricated chip has a power of 200uw @ 1.8V. that's 6.4mW altogether! I need to get down to this figure! (well..)


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ref: Fu-1993.11 tags: electrophysiology Ebner premotor motor tuning M1 date: 01-03-2012 03:34 gmt revision:1 [0] [head]

PMID-8294972 Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys. 1993

  • trained monkey to do center-out task, 48 targets (8 angles, 6 distances).
  • single-electrode recording of 197 neurons in the primary motor and secondary motor / premotor (in the superior precentral sulcus).
  • cells were mostly tuned to direction, and less to distance, in both the premovement and movement periods. distance tuning was much stronger in the movement period.
    • tuning was measure by average firing rate for the premovement, movement, and total periods.
  • long, very detailed!

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ref: -0 tags: Evarts force pyramidal tract M1 movement monkeys conduction velocity tuning date: 01-03-2012 03:25 gmt revision:3 [2] [1] [0] [head]

PMID-4966614 Relation of pyramidal tract activity to force exerted during voluntary movement.

  • One of the pioneering studies of electrophysiology in awake behaving animals; single electrode juice reward headposting: many followed.
  • {960} looked at conduction velocity, which we largely ignore now -- most highly mylenated axons are silent during motor quiescence and show phasic activity during movement.
    • Lower conduction velocity PTNs show + and - FR modulations. Again from [5]
  • [6] showed that PTN activity preceded EMG activity, implying that it was efferent rather than afferent feedback that was controlling the fr. as expected.
  • task: wrist flexion & extension under load.
  • task in monkey's home cage for a period of three months; monkeys carried out 3000 trials or more of the task (must have had strong wrists!)
  • Head fixated the monkeys for about 10 days prior unit recordings; "The monkeys learned to be quite cooperative in reentering the chair in the morning, since entrance to the chair was rewarded by the fruit juice of their choice (grape, apple, or orange). Indeed, some monkeys continued to work even in the presence of free water!
    • Maybe I should give mango some Hawaiian punch as well?
  • Mesured antidromic responses with a permanent electrode in the ipsilateral medullary pyramid.
  • Used glass insulated platinum-iridium electrodes [11]
  • traces are clean, very clean. I wonder if good insulation (in this case, glass) has anything to do with it?
  • controlled for displacement by varying the direction of load; PTNs seem to directly control muscles.
    • Fire during acceleration and movement for no load
    • Fire during load and co-contraction when loaded.
  • FR also related to δF/δt\delta F / \delta t : FR higher during a low but rising force than a high but falling force.
  • more than 100 PTN recorded from the precentral gyrus, but only 31' had clear and consistent relation to performance on the task.
    • 16 units on extension loads, 7 units flexion loads
    • It was only one joint afterall..
  • Cells responding to the same movement (flexion or extension) were often founf on the same vertical electrode tract.
  • Very little response to joint position.
  • Very clean moculations -- neurons are almost silent if there is no force production; FR goes up to 50-80Hz.
  • Prior to the exp Evart expected a position tuning model, but saw clear evidence of force tuning.
  • Group 1 muscle afferents have now been shown to project to the motor cortex of both monkey [1] and cat [9]. Make sense, as if the ctx is to control force, it needs feedback regarding its production.
  • Caveats: many muscles were involved in the study, mainly due to postural effects, and having one or two controls poorly delineates what is going on in the motor ctx.
    • Plus, all the muscles controlling the figers come into play -- the manipulandum must be gripped firmly, esp to resist extension loads.

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ref: notes-0 tags: NXT EMG design myopen date: 01-03-2012 02:49 gmt revision:33 [32] [31] [30] [29] [28] [27] [head]


devices that can be turned off & on to save power (e.g. actually disconnected from power through a P channel MOSFET. must be careful to tristate all outputs before disabling, otherwise we'll get current through the ESD protection diodes )

  1. ethernet
  2. usb reset
  3. usb host power
  4. RS 232
  5. LCD (or at least the 40ma, 6V LED -- the LCD can be disabled in software, and it only consumes 2-3 ma anyway.)
  6. core voltage boost 0.8v to 1.2V
  7. AFE

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ref: -0 tags: reinforcement learning basis function policy specialization date: 01-03-2012 02:37 gmt revision:1 [0] [head]

To read:

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ref: -0 tags: procreation babies commentary demography date: 01-03-2012 02:36 gmt revision:2 [1] [0] [head]

Demography: Babies make a comeback

  • The mathematical/ecomomic analysis of birth rates seems almost farcical to me without proper consideration of another vital point: culture. Yes, women may want to delay or renounce children to work, become more educated, travel, amass riches etc - but these are all strongly influenced by culture.
  • Another thought that they did not mention is that raising well-educated children is very expensive in developed countries - perhaps there is a tipping point where the parents have more than enough money to raise their kids to their satisfaction. (That said, I think this is less than likely given that parents are very competitive, at least in the US, with the education and support of their children).
  • Perhaps some understanding of why people in developed countries have children in the first place is warranted. I might recommend asking them to find out :-) Such information would help any purely economic theory.

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ref: Shuler-2006.03 tags: reward V1 visual cortex timing reinforcement surprising date: 01-03-2012 02:33 gmt revision:4 [3] [2] [1] [0] [head]

PMID-16543459[0] Reward Timing in the Primary Visual Cortex

  • the responses of a substantial fraction of neurons in the primary visual cortex evolve from those that relate solely to the physical attributes of the stimuli to those that accurately predict the timing of reward.. wow!
  • rats. they put goggles on the rats to deliver full-fields retinal illumination for 400ms (isn't this cheating? full field?)
  • recorded from deep layers of V1
  • sensory processing does not seem to be reliable, stable, and reproducible...
  • rewarded only half of the trials, to see if the plasticity was a result of reward delivery or association of stimuli and reward.
  • after 5-7 sessions of training, neurons began to respond to the poststimulus reward time.
  • this was actually independent of reward delivery - only dependent on the time.
  • reward-related activity was only driven by the dominant eye.
  • individual neurons predict reward time quite accurately. (wha?)
  • responses continued even if the animal was no longer doing the task.
  • is this an artifact? of something else? what's going on? the suggest that it could be caused by subthreshold activity due to recurrent connections amplified by dopamine.


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ref: Sergio-1997.08 tags: M1 force tuning kinematics dynamics Kalaska date: 01-03-2012 02:31 gmt revision:1 [0] [head]

PMID-9307146[0] Systematic changes in directional tuning of motor cortex cell activity with hand location in the workspace during generation of static isometric forces in constant spatial directions.

  • The discharge rate of all proximal-arm M1 cells was affected by both hand location and by the direction of static force. w/ interaction between force direction and hand location.
    • this is consistent with cortical units controlling muscle activity directly or through the spinal cord.
  • conclusion: M1 controls muscles directly and contributes to the transformation from extrinsic coordinates to muscle activations while coordinating limb movements.


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ref: Shulgina-1986.09 tags: reinforcement learning review date: 01-03-2012 02:31 gmt revision:5 [4] [3] [2] [1] [0] [head]

Reinforcement learning in the cortex (a web scour/crawl):

  • http://www.springerlink.com/content/v211201413228x34/
    • short/long interspike intervals via pain reinforcement in immobilized rabbits.
  • PMID-3748636 Increased regularity of activity of cortical neurons in learning due to disinhibitory effect of reinforcement.
    • more rabbit shocking.
  • http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T0F-3S1PT00-P
    • applied glutamate & noradrenaline; both responses are complex.
  • Reinforcement learning in populations of spiking neurons
    • the result: reinforcement learning can function effectively in large populations of neurons if there is a trace of the population activity in addition to the reinforcement signal. this trace must be per-synapes or perhaps per-neuron (as has been anticipated for some time). very important result, helps with the 'specificity' problem.
    • in human terms, the standard reinforcement learning approach is analogous to having a class of students write an exam and being informed by the teacher on the next day whether the majority of students passed or not.
    • this learning method is slow and achieves limited fidelity; in contrast, behavioral reinforcement learning can be reliable and fast. (perhaps this is a result of already-existing maps and or activity in the cortex?)
    • reinforcement learning is almost the opposite of backpropagation, in that in backprop, a error signal is computed per neuron, while in reinforcement learning the error is only computed for the entire system. They posit that there must be a middle ground (need something less than one neuron to compute the training/error signal per neuron, othewise the system would not be very efficient...)
    • points out a good if obvious point: to learn from trial and error different responses to a given stimulus must be explored, and, for this, randomness in the neural activities provides a convenient mechanism.
    • they use the running mean as an eligibility trace per synapse. then change in weight = eta * eligibility trace(t), evaluated at the ends of trials.
    • implemented an asymmetric rule that updates the synapses only slightly if the output is reliable and correct.
    • also needed a population signal or fed-back version of the previous neural behavior. Then individual reinforcement is a product of the reinforcement signal * the population signal * the eligibility trace (the last per synapse). Roughly, if the population signal is different than the eligability trace, and the behavior is wrong, then that synapse should be reinforced. and vice-versa.
  • PMID-17444757 Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity.
    • seems to give about the same result as above, except with STDP: reinforcement-modulated STDP with an eligibility trace stored at each synapse permits learning even if a reward signal is delayed.
    • network can learn XOR problem with firing-rate or temporally coded input.
    • they want someone to look for reward-moduled STDP. paper came out June 2007.
  • PMID: Metaplasticity: the plasticity of synaptic plasticity (1996, Mark Bear)
    • there is such thing as metaplasticity! (plasticity of plasticity, or control over how effective NMDAR are..)
    • he has several other papers on this topic after this..
  • PMID-2682404 Reward or reinforcement: what's the difference? (1989)
    • reward = certain environmental stimuli have the effect of eliciting approach responses. ventral striatum / nucleus accumbens is instrumental for this.
    • reinforcement = the tendency of certain stimuli to strengthen stimulus-response tendencies. dorsolateral striatum is used here.
  • PMID-9463469 Rapid plasticity of human cortical movement representation induced by practice.
  • used TMS to evoke isolated and directionally consistent thumb movements.
  • then asked the volunteers to practice moving their thumbs in an opposite direction
  • after 5-30 minutes of practice, then TMS evoked a response in the practiced direction. wow! this may be short-term memory or the first step in skill learning.
  • PMID-12736341 Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity.
    • temporally asymmetric plasticity is apparently required for a stable network (aka no epilepsy?), and can be optimized to represent the temporal structure of input correlations.

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ref: Cheney-1980.1 tags: M1 kinematics dynamics tuning STA EMG Fetz date: 01-03-2012 02:30 gmt revision:3 [2] [1] [0] [head]

PMID-6253605[0] Functional classes of primate corticomotoneuronal cells and their relation to active force

  • monkeys made ramp and hold torque wrist movements/contractions.
  • corticomotoneuronal cells identified by clear postspike facilitation of rectified EMG activity.
  • all CM cells or PTNs were related to force - with a mixture/diversity of phasic, tonic, and ramp discharge rate profiles.
  • torque trajectory rather than velocity signal seems to be the primnary determinant of cell firing rate...
  • cells appear to be recruited at low force levels..with increasing rates as the torque increases.
  • high firing rates observed > 100!
    • and really low firing rate when there was no torque.


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ref: Fu-1995.02 tags: M1 motor tuning kinematics dynamic direction date: 01-03-2012 02:21 gmt revision:1 [0] [head]

PMID-7760138[0] Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons

  • 48 target 2D center out task
  • wanted to disambiguate temporal aspects of tuning vs. parallel (e.g. across a neuronal population) aspects of tuning.
  • On average we found a clear temporal segregation and ordering in the onset of the parameter-related partial R2 values: direction-related discharge occurred first (115 ms before movement onset), followed sequentially by target position (57 ms after movement onset) and movement distance (248 ms after movement onset).
  • therefore, the motor cortex seems to have strong temporal processing aspects. duh.
    • Probably explained by Todorov ...


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ref: Merletti-2009.02 tags: surface EMG multielectrode recording technology italy date: 01-03-2012 01:07 gmt revision:2 [1] [0] [head]

PMID-19042063[0] Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art

  • good background & review of surface EMG (sEMG) - noise levels, electrodes, electronics. eg. Instrumentation amplifiers with an input resistance < 100MOhm are not recommended, and the lower the input capacitance, the better: the impedance of a 10pf capacitor at 100hz is 160MOhm.
  • Low and balanced input impedances are required to reduce asymmetric filtering of common-mode power-line noise.


[0] Merletti R, Botter A, Troiano A, Merlo E, Minetto MA, Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art.Clin Biomech (Bristol, Avon) 24:2, 122-34 (2009 Feb)

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ref: Lemon-1976.1 tags: Lemon motor recording afferent input date: 01-03-2012 01:01 gmt revision:1 [0] [head]

PMID-11491[0] Afferent input to movement-related precentral neurones in conscious monkeys.

  • Trained monkeys to make both a stereotyped movement and respond passively and calmly to external stimulation.
  • Most cells recorded responded to joint velocity; none to joint position.
  • A smaller subset responded to muscle palpitation
  • Cells were tuned to similar things as their neighbors, though sometimes they responded to markedly different stimuli. Consistent with Wyler.


[0] Lemon RN, Porter R, Afferent input to movement-related precentral neurones in conscious monkeys.Proc R Soc Lond B Biol Sci 194:1116, 313-39 (1976 Oct 29)

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ref: Fan-2011.01 tags: TBSI wireless recordings system FM modulation multiplexing poland date: 01-03-2012 00:55 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-21765934[0] A wireless multi-channel recording system for freely behaving mice and rats.

  • Light enough that rats can use it: 4.5g
  • 15 or 32 channels.
  • Good list of the competiton; they note Szuts et al [31], [1], {1003}, [2], {1004}, {1005}
  • Why are there so many authors?
  • Morizio and Henry Yin last authors.


[0] Fan D, Rich D, Holtzman T, Ruther P, Dalley JW, Lopez A, Rossi MA, Barter JW, Salas-Meza D, Herwik S, Holzhammer T, Morizio J, Yin HH, A wireless multi-channel recording system for freely behaving mice and rats.PLoS One 6:7, e22033 (2011)
[1] no Title no Source no Volume no Issue no Pages no PubDate
[2] Szuts TA, Fadeyev V, Kachiguine S, Sher A, Grivich MV, Agrochão M, Hottowy P, Dabrowski W, Lubenov EV, Siapas AG, Uchida N, Litke AM, Meister M, A wireless multi-channel neural amplifier for freely moving animals.Nat Neurosci 14:2, 263-9 (2011 Feb)

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ref: Akin-1995.06 tags: Najafi neural recording technology micromachined digital TETS 1995 PNS schematics date: 01-01-2012 20:23 gmt revision:8 [7] [6] [5] [4] [3] [2] [head]

IEEE-717081 (pdf) An Implantable Multichannel Digital neural recording system for a micromachined sieve electrode

  • Later pub: IEEE-654942 (pdf) -- apparently putting on-chip isolated diodes is a difficult task.
  • 90mw of power @ 5V, 4x4mm of area (!!)
  • targeted for regenerated peripheral neurons grown through a micromachined silicon sieve electrode.
    • PNS nerves are deliberately severed and allowed to regrow through the sieve.
  • 8bit low-power current-mode ADC. seems like a clever design to me - though I can't really follow the operation from the description written there.
  • class e transmitter amplifier.
  • 3um BiCMOS process. (you get vertical BJTs and Zener diodes)
  • has excellent schematics. - including the voltage regulator, envelop detector & ADC.
  • most of the power is dissipated in the voltage regulator (!!) - 80mW of 90mW.
  • tiny!
  • rather than using pseudoresistors, they use diode-capacitor input filter which avoids the need for chopping or off-chip hybrid components.
  • can record from any two of 32 input channels. I think the multiplexer is after the preamp - right?


Akin, T. and Najafi, K. and Bradley, R.M. Solid-State Sensors and Actuators, 1995 and Eurosensors IX.. Transducers '95. The 8th International Conference on 1 51 -54 (1995)

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ref: bookmark-0 tags: machine_learning research_blog parallel_computing bayes active_learning information_theory reinforcement_learning date: 12-31-2011 19:30 gmt revision:3 [2] [1] [0] [head]

hunch.net interesting posts:

  • debugging your brain - how to discover what you don't understand. a very intelligent viewpoint, worth rereading + the comments. look at the data, stupid
    • quote: how to represent the problem is perhaps even more important in research since human brains are not as adept as computers at shifting and using representations. Significant initial thought on how to represent a research problem is helpful. And when it’s not going well, changing representations can make a problem radically simpler.
  • automated labeling - great way to use a human 'oracle' to bootstrap us into good performance, esp. if the predictor can output a certainty value and hence ask the oracle all the 'tricky questions'.
  • The design of an optimal research environment
    • Quote: Machine learning is a victim of it’s common success. It’s hard to develop a learning algorithm which is substantially better than others. This means that anyone wanting to implement spam filtering can do so. Patents are useless here—you can’t patent an entire field (and even if you could it wouldn’t work).
  • More recently: http://hunch.net/?p=2016
    • Problem is that online course only imperfectly emulate the social environment of a college, which IMHO are useflu for cultivating diligence.
  • The unrealized potential of the research lab Quote: Muthu Muthukrishnan says “it’s the incentives”. In particular, people who invent something within a research lab have little personal incentive in seeing it’s potential realized so they fail to pursue it as vigorously as they might in a startup setting.
    • The motivation (money!) is just not there.

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ref: Atallah-2007.01 tags: striatum skill motor learning VTA substantia nigra basal ganglia reinforcement learning date: 12-31-2011 18:59 gmt revision:3 [2] [1] [0] [head]

PMID-17187065[0] Separate neural substrates for skill learning and performance in the ventral and dorsal striatum.

  • good paper. via SCLin's blog. slightly confusing anatomical terminology.
  • tested in rats, which has a anatomically different basal ganglia system than primates.
  • Rats had to choose which driection in a Y maze based on olfactory cues. Normal rats figure it out in 60 trials.
  • ventral striatum (nucleus accumbens here in rats) connects to the ventral prefrontal cortices (for example, the orbitofrontal cortex)
    • in primates, includes the medial caudate, which has been shown in fMRI to respond to reward prediction error. Neural activity in the caudate is attenuated when a monkey reaches optimal performance.
  • dorsal parts of the striatum (according to web: caudate, putamen, globus pallidus in primates) connect to the dorsal prefrontal and motor cortices
    • (according to them:) this corresponds to the putamen in primates. Activity in the putamen reflects performance but not learning.
    • activity in the putamen is highest after successful learning & accurate performance.
  • used muscimol (GABAa agonist, silences neural activity) and AP-5 (blocks NMDA based plasticity), in each of the target areas.
  • dorsal striatum is involved in performance but not learning
    • Injection of muscimol during acquisition did not impair test performance
    • Injection of muscimol during test phase did impair performance
    • Injection of AP-5 during acquisition had no effect.
    • in acquisition sessions, muscimol blocked instrumental response (performance); but muscimol only has a small effect when it was injected after rats perfected the task.
      • Idea: consistent behavior creates a stimulus-response association in extrastriatal brain areas, e.g. cerebral cortex. That is, the basal ganglia is the reinforcement signal, the cortex learns the association due to feedback-driven behavior? Not part of the habit system, but make and important contribution to goal-directed behavior.
      • This is consistent with the observation that behavior is initially goal driven but is later habitual.
    • Actually, other studies show that plasticity in the dorsal striatum may be detrimental to instrumental learning.
    • The number of neurons that fire just before the execution of a response is larger in the putamen than the caudate.
  • ventral striatum is involved in learning and performance.
    • Injection of AP-5 or muscimol during acquisition (learning behavior) impairs test performance.
    • Injection of AP-5 during test performance has no effect , but muscimol impairs performance.
  • Their data support an actor-director-critic architecture of the striatum:
    • Actor = dorsal striatum; involved in performance, but not in learning them.
    • Director = ventral striatum; quote "it somehow learns the relevant task demands and directs the dorsal striatum to perform the appropriate action plans, but, crucially, it does not train the dorsal striatum"
      • ventrai striatum acts through the orbitofrontal cortex that mantains representations of task-reward contingencies.
      • ventral striatum might also select action selection through it's projections to the substantia nigra.
    • Critic = dopaminergic inputs from the ventral tegmental area and substantia nigra.


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ref: Obeid-2004.02 tags: Wolf BMI recording electronics telemetry Obeid date: 12-31-2011 18:27 gmt revision:4 [3] [2] [1] [0] [head]

PMID-14757341[1] A low power multichannel analog front end for portable neural signal recordings.

  • have an interesting section on CMRR, quote: Although we use a precision differential amplifier with a CMRR of 110 dB, we were unable, in practice, to measure CMRRs greater than not, vert, similar42 dB. This can be accounted for by the device tolerances in the preamplifier stage; using ±0.1% resistors and ±5% capacitors in the preamplifier, the expected worst case CMRR at 1 kHz is 39.2 dB


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ref: PENFIELD-1963.12 tags: Penfield memory stimulation music epilepsy awesome date: 12-29-2011 22:21 gmt revision:4 [3] [2] [1] [0] [head]

PMID-14090522[0] The Brains record of auditory and visual experience -- A final summary and discussion

  • 102 pages. basically, this is a book.
  • Electrical stimulation causes 'hallucinations of things previously seen or heard or experienced.'
    • 'Both the experience and the interpretation are produced by discharge in the temporal cortex and not in other areas'
    • Experiential responses only followed stimulation in the temporal lobe.
  • These tests were done in an effort to locate the source of a seizure.
  • Damn, references the Caliph at Cordova as the first to document epileptic hallucinations (1519!)
    • And Hughlings Jackson (1888)!
  • Extirpation of memories elicited by ustim to an area persist after removal of that area.
  • Performed 1,288 surgeries, 520 of them for seizures in the temporal lobes, 40 of these with experiential responses, and 24 of those with experiential epileptic hallucinations.
    • Many of the patients' epilepsy was caused by ischemia, perhaps developmental; others by glioma..
  • Stimulation of the white matter has never produced an experiential response. Deep stimulation in the amygdala or hippocampus (??) also failed to elicit experiential responses.
  • Talks about 'difficult birth' -- was/is this the cause of some epilepsy? Or has that been discounted?
  • Buncha stuff on human cortical anatomy / topology, which is not so interesting to me.
  • Walker on the chimpanzee (1938) showed that the temporal cortex has no direct connections to the thalamus except posteriorly, where projections are received from nucleus lateralis posterior and pulvinar (visual attention), and within the transverse temporal gyri which receive auditory afferent projections from the medial geniculate body.
    • Also receives large fiber projections from the hippocampus.
  • This is absolutely fascinating. Memories, art, songs (music, so much music -- temporal lobe!), childbirth, counting, childhood molestation, a whole host of experiences were brought forth by electrical stimulation.
    • Case 9. E. Le. This 44-year old woman began to have seizures at age 22 during a pregnancy. The attack pattern was: (1) flushing of face and neck (2) automatism; (3) occasional generalized seizure. During and automatism she was apt to say, "I am alright". Then she would walk about the room and show marked affection toward anyone who happened to be present.
    • Repeated without warning: She said, "Yes, another experience, a different experience." Then she added, "A true experience. This man, Mr. Meerburger, he, oh well, he drinks. Twice his boy has run away. I went to the store once for an ice cream cone and I saw that he was back, and I said 'Hmm, he is back,' and the lady asked me 'What is the matter,' and I didn't know how to explain so I said, 'Well you know Mr. Meerburger drinks.' I thought that was the easiest way but later mother told me, no, and it made it a lot worse."
    • What surprises me is the relative lack of breadth in these --many of the responses to stimulation are quite similar, over a wide range of cortex, many of them very dream-like in features and recall.
      • Their impression: It is often evident that ''each stimulation leaves behind a facilitating influence so that the same response follows each stimulation and this facilitation may cause a given response to follow stimulation at one to three centimeters distance. Illustrated by the case 5, D.F
      • This deserves far more experimentation! E.g. ask the patient to think about something, and see if the same stimulation elicits different memories.
    • Another patient had a series of experiential hallucinations which all involved some aspect of 'grabbing' -- a man grabbing a rifle from a cadet during a parade, a man snatching his hat from the hat-check girl, grabbing a stick from a dog's mouth. In this epileptic, an instance of 'grabbing' was the ictal focus. Amazing.
  • Points out that stimulation must activate a great number of neural circuits, only one specific memory is recalled -- indicating that there is strong inhibition for mutual-exclusion.
  • Non-dominant, non-speech temporal cortex is almost always involved in interpretation: stimulation produces visual experiences, or visual interpretive illusions (change in distance or speed).
    • Stimluation also produces changes in the state-of-mind.
  • Certain sorts of experiences seem absent:
    • The times of making up ones mind
    • Times of carrying out skilled acts, writing messages or adding figures,
    • Eating food
    • Sexual excitement and experience (unless the patients may have self-censored this?)
    • Intense pain or suffering.
    • These things do not involve interpretation, and the focus of attention is not on the way that things are heard or seen.
  • They would remove quite large sections of the temporal lobe!
    • Still, the excision of these areas does not abolish memory: it does not contain a record of the past.
    • Yet stimulation in the temporal lobe recalls memories as nowhere else does.
  • There is a sharp frontier / boundary between auditory and visual temporal cortices and interpretive -- millimeters movement may change phosphenes into recall of a familiar person.
  • Note the comparison between speech cortex (dominant) and interpretive -- stimulation of speech cortex produces no speech, only aphasia, whereas stimulation of non-dominant termporal cortex forces recall.
  • "He who is faithfully analysing many cases of epilepsy is doing far more than studying epilepsy"



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ref: OLDS-1954.12 tags: Olds Milner operant conditioning electrical reinforcement wireheading BMI date: 12-29-2011 05:09 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-13233369[0] Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain.

  • The original electrical reinforcement experiment!
  • tested out various areas for reinforcement; septal forebrain area was the best.
  • later work: 1956 Olds, J. Runway and maze behavior controlled by basomedial forebrain stimulation in the rat. J. Comp. Physiol. Psychol. 49:507-12.


[0] OLDS J, MILNER P, Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain.J Comp Physiol Psychol 47:6, 419-27 (1954 Dec)

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ref: Wyrwicka-1966.01 tags: ICMS brainstem stimulation feeding prey chasing VTA date: 12-28-2011 20:44 gmt revision:2 [1] [0] [head]

PMID-5941514[0] Feeding induced in cats by electrical stimulation of the brain stem.

  • tested in cats.
  • stimulation points in the lateral hypothalamus (makes sense, controlls hunger)
  • half in ventral tegmental area (VTA)
  • aphygia is induced by lesions of the lateral hypothalamus.
  • in one experiment, the meat in the bowl was replaced with a banana. "Upon stimulation the cat quickly approached the bowl, sniffed the banana, turned away (in some disgust and frustration!?), searched the chamber, returned to the banana etc, but would not eat the banana."


[0] Wyrwicka W, Doty RW, Feeding induced in cats by electrical stimulation of the brain stem.Exp Brain Res 1:2, 152-60 (1966)

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ref: Nicolelis-1998.11 tags: spatiotemporal spiking nicolelis somatosensory tactile S1 3b microwire array rate temporal coding code date: 12-28-2011 20:42 gmt revision:3 [2] [1] [0] [head]

PMID-10196571[0] Simultaneous encoding of tactile information by three primate cortical areas

  • owl monkeys.
  • used microwires arrays to decode the location of tactile stimuli; location was encoded through te population, not within single units.
  • areas 3b, S1 & S2.
  • used LVQ (learning vector quantization) backprop, LDA to predict/ classify touch trials; all yielded about the same ~60% accuracy. Chance level 33%.
  • Interesting: "the spatiotemporal character of neuronal responses in the SII cortex was shown to contain the requisite information for the encoding of stimulus location using temporally patterned spike sequences, whereas the simultaneously recorded neuronal responses in areas 3b and 2 contained the requisite information for rate coding."
    • They support this result by varying bin widths and looking at the % of correctly classivied trials. in SII, increasing bin width decreases (slightly but significantly) the prediction accuracy.


[0] Nicolelis MA, Ghazanfar AA, Stambaugh CR, Oliveira LM, Laubach M, Chapin JK, Nelson RJ, Kaas JH, Simultaneous encoding of tactile information by three primate cortical areas.Nat Neurosci 1:7, 621-30 (1998 Nov)

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ref: -0 tags: Scott M1 motor control pathlets filter EMG date: 12-22-2011 22:52 gmt revision:1 [0] [head]

PMID-19923243 Complex Spatiotemporal Tuning in Human Upper-Limb Muscles

  • Original idea: M1 neurons encode 'pathlets', sophisticated high-level movement trajectories, possibly through the action of both the musculoskeletal system and spinal cord circuitry.
  • Showed that muscle pathlets can be extracted from EMG data, relkiably and between patients, implying that M1 reflects 'filter-like' properties of the body, and not high level representations.

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ref: Schultz-1998.07 tags: dopamine reward reinforcement_learning review date: 12-07-2011 04:16 gmt revision:1 [0] [head]

PMID-9658025[0] Predictive reward signal of dopamine neurons.

  • hot article.
  • reasons why midbrain Da is involved in reward: lesions, receptor blocking, electrical self-stimulation, and drugs of abuse.
  • DA neurons show phasic response to both primary reward and reward-predicting stimul.
  • 'All responses to rewards and reward-predicting stimuli depend on event predictability.
  • Just think of the MFB work with the rats... and how powerful it is.
  • most deficits following dopamine-depleting lesions are not easily explained by a defective reward signal (e.g. parkinsons, huntingtons) -> implying that DA has two uses: the labeling of reward, that the tonic enabling of postsynaptic neurons.
    • I just anticipated this, which is good :)
    • It is still a mystery how the neurons in the midbrain determine to fire - the pathways between reward and behavior must be very carefully segregated, otherwise we would be able to self-simulate
      • the pure expectation part of it is bound play a part in this - if we know that a certain event will be rewarding, then the expectation will diminish DA release.
  • predictive eye movements amerliorate behavioral perfromance through advance focusing. (interesting)
  • predictions are used in industry:
    • Internal Model Control is used in industry to predict future system states before they actually occur. for example, the fly-by-wire technique in aviation makes decisions to do particular manuvers based on predictable forthcoming states of the plane. (Like a human)
  • if you learn a reaction/reflex based on a conditioned stimulus, the presentation of that stimulus sets the internal state to that motivated to achieve the primary reward. there is a transfer back in time, which, generally, is what neural systems are for.
  • animals avoid foods that fail to influence important plasma/brain parameters, for example foods lacking essential amino acids like histidine, threonine, or methionine. In the case of food, the appearance/structure would be used to predict the slower plasma effects, and hence influence motivation to eat it. (of course!)
  • midbrain groups:
    • A8 = dorsal to lateral substantia nigra
    • A9 = pars compacta of substantia nigra, SNc
    • A10 = VTA, media to substantia nigra.
  • The characteristic polyphasic, relatively long impulses discharged at low frequencies make dpamine neurons easily distinguishable from other midbrain neurons.


[0] Schultz W, Predictive reward signal of dopamine neurons.J Neurophysiol 80:1, 1-27 (1998 Jul)

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ref: Loewenstein-2006.1 tags: reinforcement learning operant conditioning neural networks theory date: 12-07-2011 03:36 gmt revision:4 [3] [2] [1] [0] [head]

PMID-17008410[0] Operant matching is a generic outcome of synaptic plasticity based on the covariance between reward and neural activity

  • The probability of choosing an alternative in a long sequence of repeated choices is proportional to the total reward derived from that alternative, a phenomenon known as Herrnstein's matching law.
  • We hypothesize that there are forms of synaptic plasticity driven by the covariance between reward and neural activity and prove mathematically that matching (alternative to reward) is a generic outcome of such plasticity
    • models for learning that are based on the covariance between reward and choice are common in economics and are used phenomologically to explain human behavior.
  • this model can be tested experimentally by making reward contingent not on the choices, but rather on the activity of neural activity.
  • Maximization is shown to be a generic outcome of synaptic plasticity driven by the sum of the covariances between reward and all past neural activities.


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ref: bookmark-0 tags: EMG SNR bits delsys differential amplifier bandwidth date: 12-07-2011 03:15 gmt revision:4 [3] [2] [1] [0] [head]


  • on a very good EMG recording the signal-to-noise is 65db ~= 11 bits
  • dynamic range of 5uv to 10mv.
  • differential measurement essential.
  • googling 'EMG bandwidth' yields something around 20-500hz. study of this question
  • delsys wireless EMG system & logger - uses WLAN to transmit the data (up to 16 channels) passband 20-450hz, has QVGA screen, 1GB removable storage.
  • also see "grasp recognition from myoelectric signals" images/474_1.pdf

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ref: BuzsAki-1996.04 tags: hippocampus neocortex theta gamma consolidation sleep Buzsaki review learning memory date: 12-07-2011 02:31 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-8670641[0] The hippocampo-neocortical dialogue.

  • the entorhinal ctx is bidirectionally conneted to nearly all areas of the neocortical mantle.
  • Buzsaki correctly predicts that information gathered during exploration is played back at a faster scale during synchronous population busts during (comnsummatory) behaviors.
  • looks like a good review of the hippocampus, but don't have time to read it now.
  • excellent explanation of the anatomy (with some omissions, click through to read the caption):
  • SPW = sharp waves, 40-120ms in duration. caused by synchronous firing in much of the cortex ; occur 0.02 - 3 times/sec in daily activity & during slow wave sleep.
    • BUzsaki thinks that this may be related to memory consolidation.
  • check the cited-by articles : http://cercor.oxfordjournals.org/cgi/content/abstract/6/2/8
[0] Buzsaiki G, The hippocampo-neocortical dialogue.Cereb Cortex 6:2, 81-92 (1996 Mar-Apr)

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ref: Carlton-1981.1 tags: visual feedback 1981 error correction movement motor control reaction time date: 12-06-2011 06:35 gmt revision:1 [0] [head]

PMID-6457106 Processing visual feedback information for movement control.

  • Vusual feedback can correct movement within 135ms.
  • Measured this by simply timing the latency from presentation of visual error to initiation of corrective movement.

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ref: -0 tags: rap poem life philosophy date: 04-07-2011 15:39 gmt revision:1 [0] [head]

so son why you looking so glum
why you not making that symbol with your index finger and thumb?
yea boy i know she couldn't kickit for real
true true puddin, if fun were a sale then she got quite a steal

the thing about the brain is with physical consequence
get computed with aplomb, clarity and consonance
when emotional matters get sucked into the fray
only a fool will guess where the causality will stray

it's almost fucking impossible to disentangle yourself
which is why im yappin to you rather than to cough cough to myself
dissatisfaction with her life is only slightly attached to angst in yer life
but blaming yourself is not what she did, so don't do it. 

externalize events, it's a common adult strategy
makes you feel a lot better irrespective of causality
titrating the blame like ah chemistry class
it's like the assholes are those who don't look at their own ass

speakin of which, point your telescope over there, 
no not the squirrel, check the thing that fills out the chair
now amusements can be had, amusements are given
the butcher is calling, high time to make a killin

you don't need to go to walking the Walden like Thoreau
to pick up the coinage that self questioning will throw
those who fail this will be a needle-nosed stasis
while you me and charley will be making a praxis

alright my brother time to show that squirrel who's bonafide
self-bootstrapping is best done while physically occupied. 

just having some fun :-)

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ref: work-0 tags: kinarm problem mathML date: 11-03-2010 16:05 gmt revision:8 [7] [6] [5] [4] [3] [2] [head]

Historical notes from using the Kinarm... this only seems to render properly in firefox / mozilla.

To apply cartesian force fields to the arm, the original kinarm PLCC (whatever that stands for) converted joint velocities to cartesian veolocities using the jacobian matrix. All well and good. The equation for endpoint location of the kinarm is:

x^=[l 1sin(θ sho)+l 2sin(θ sho+θ elb) l 1cos(θ sho)+l 2cos(θ sho+θ elb)] \hat{x} = { \left[ \array{ l_1 sin(\theta_{sho}) + l_2 sin(\theta_{sho} + \theta_{elb} ) \\ l_1 cos(\theta_{sho}) + l_2 cos(\theta_{sho} + \theta_{elb} ) } \right] }

L_1 = 0.115 meters, l_2 = 0.195 meters in our case. The jacobian of this function is: J=[l 1sin(θ sho)l 2sin(θ sho+θ elb) l 2sin(θ elb) l 1cos(θ sho)+l 2cos(θ sho+θ elb) l 2cos(θ elb)] J = { \left[ \array{ - l_1 sin(\theta_{sho}) - l_2 sin(\theta_{sho} + \theta_{elb} ) && - l_2 sin(\theta_{elb}) \\ l_1 cos(\theta_{sho}) + l_2 cos(\theta_{sho} + \theta_{elb} ) && l_2 cos(\theta_{elb}) } \right] } v^=Jθ^ \hat{v} = J \cdot \hat{\theta} etc. and (I think!) F^=Jτ^ \hat{F} = J \cdot \hat{\tau} where tau is the shoulder and elbow torques and F is the cartesian force. The flow of the PLCC is then:

  1. convert joint angluar velocities to cartesian velocities
  2. cartesian velocities to cartesian forces by a symmetric matrix A which effects simple viscious and curl fields.
F^=Av^ \hat{F} = A \cdot \hat{v}
  1. cartesian forces to joint torques via the inverse of the jacobian.
But, and I may be wrong here, rather than inverting the jacobian, the PLCC simply takes the transform. The inverse of the jacobian and the transpose are not even close to equal. viz (from mathworld):

J=[a b c d] J = { \left[ \array{ a & b \\ c & d } \right] }

J 1=1adbc[d b c a][a c b d]=J T J^{-1} = \frac{ 1}{a d - b c} { \left[ \array{d &-b \\ -c & a} \right] } \ne { \left[ \array{a & c \\ b & d} \right] } = J^{T}

substitute to see if the matrices look similar ...

|J|[l 2cos(θ elb) l 2sin(θ elb) l 1cos(θ sho)l 2cos(θ sho+θ elb) l 1sin(θ sho)l 2sin(θ sho+θ elb)][l 1sin(θ sho)l 2sin(θ sho+θ elb) l 1cos(θ sho)+l 2cos(θ sho+θ elb) l 2sin(θ elb) l 2cos(θ elb)]{\vert J \vert} \cdot { \left[ \array{ l_2 cos(\theta_{elb}) && l_2 sin(\theta_{elb}) \\ - l_1 cos(\theta_{sho}) - l_2 cos(\theta_{sho} + \theta_{elb} ) && - l_1 sin(\theta_{sho}) - l_2 sin(\theta_{sho} + \theta_{elb} ) } \right] } \ne { \left[ \array{ - l_1 sin(\theta_{sho}) - l_2 sin(\theta_{sho} + \theta_{elb} ) && l_1 cos(\theta_{sho}) + l_2 cos(\theta_{sho} + \theta_{elb} ) \\ - l_2 sin(\theta_{elb}) && l_2 cos(\theta_{elb}) } \right] }


|J|=l 1l 2sin(θ sho)cos(θ elb)l 2 2sin(θ sho+θ elb)cos(θ elb)+l 1l 2cos(θ sho)sin(θ elb)l 2 2cos(θ sho+θ elb)sin(θ elb) {\vert J \vert} = { - l_1 l_2 sin(\theta_sho) cos(\theta_elb) - l_2^2 sin(\theta_{sho} + \theta_{elb} ) cos(\theta_elb) + - l_1 l_2 cos(\theta_sho) sin(\theta_elb) - l_2^2 cos(\theta_{sho} + \theta_{elb} ) sin(\theta_elb) }

I'm surprised that we got something even like curl and viscous forces - the matrices are not similar. This explains why the forces seemed odd and poorly scaled, and why the constants for the viscious and curl fields were so small (the units should have been N/(cm/s) - 1 newton is a reasonable force, and the monkey moves at around 10cm/sec, so the constant should have been 1/10 or so. Instead, we usually put in a value of 0.0005 ! For typical values of the shoulder and elbow angles, the determinant of the matrix is 200 (the kinarm PLCC works in centimeters, not meters), so the transpose has entries ~ 200 x too big. Foolishly we compensated by making the constant (or entries in A) 200 times to small. i.e. 1/10 * 1/200 = 0.0005 :(

The end result is that a density-plot of the space spanned by the cartesian force and velocity is not very clean, as you can see in the picture below. The horizontal line is, of course, when the forces were turned off. A linear relationship between force and velocity should be manifested by a line in these plots - however, there are only suggestions of lines. The null field should have a negative - slope line in upper left and lower right; the curl field should have a positive sloped line in the upper right and negative in the lower left (or vice-vercia).


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ref: work-0 tags: emg_dsp design part selection stage6 date: 09-22-2010 20:09 gmt revision:9 [8] [7] [6] [5] [4] [3] [head]

"Stage 6" part selection:

  • B527 to replace the BF537 -- big difference are more pins + USB OTG high-speed port. The previous deign used Maxim's MAX3421E, which seems to drop packets / have limited bandwidth (or perhaps my USB profile is incorrect?)
    • available in both 0.8mm and 0.5mm BGA. which? both are available from Digi-key. Coarser one is fine, will be easier to route.
    • Does not support mobile SDRAM nor DDR SDRAM; just the vanilla variety.
  • Continue to use the BF532 on the wireless devices (emg, neuro)
  • LAN8710 to replace the LAN83C185. Both can use the MII interface; the LAN83 is not recommended for new designs, though it is in the easier-to-debug TQFP package. Blackfin EZ-KIT for BF527 uses the LAN8710.
    • comes in 0.5mm pitch QFN-32 package.
    • 3.3V and 1.2V supply - can supply 1.2V externally.
  • SDRAM: MT48LC16M16A2BG-7E:D, digikey 557-1220-1-ND 16M x16, or 4M x 16 bit X 4 banks.
    • VFBGA-54 package.
    • 3.3v supply.
  • converter: AD7689 8 channel, 16-bit SAR ADC. has a built-in sequencer, which is sweet. (as well as a temperature sensor??!)
    • Package: 20LFCSP.
    • Seems we can run it at 4.0V, as in stage4.
  • Inst amp: MCP4208, available MSOP-8 (they call it 8-muMax). can use the same circuitry as in stage2 - just check the bandwidth; want 2khz maybe?
  • M25P16 flash, same as on the dev board.
    • Digikey M25P16-VMN6P-ND : 150mil width SOIC-8
  • USB: use the on-board high-speed controller. No need for OTG functionality; FCI USB connector is fine. Digikey 609-1039-ND.

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ref: -0 tags: artificial intelligence Hutters theorem date: 08-05-2010 05:06 gmt revision:0 [head]

Hutter's Theorem: for all problems asymptotically large enough, there exists one algorithm that is within a factor of 5 as fast as the fastest algorithm for a particular problem. http://www.hutter1.net/ai/pfastprg.htm

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ref: OSuilleabhain-1998.11 tags: analysis tremor parkinsons disease date: 07-19-2010 19:22 gmt revision:2 [1] [0] [head]

PMID-9827772[0] Time-frequency analysis of tremor

  • Frequency of tremor in non-attended, non-tapping leg and arm changed frequency and synchronized:
    • For example, arm and leg tremors at 5.2 and 3.8 Hz, respectively, shifted to a common frequency of 4.6 Hz in one Parkinsons disease patient while using the contralateral arm to perform a tapping movement in time with a metronome at 2 Hz.
    • Psychogneic tremor was sychronized to the metronome in normal volunteers (e.g. 2Hz or 4Hz).
  • PSD was estimated via the welch method of averaging periodograms (FFT length 128, kaiser window segments overlapping 50%)
  • Also used the wigner method for tracking frequency changes in the tremor; this yeilded estimates every 0.5s with 0.1Hz resolution.


[0] O'Suilleabhain PE, Matsumoto JY, Time-frequency analysis of tremors.Brain 121 ( Pt 11)no Issue 2127-34 (1998 Nov)

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ref: notes-0 tags: Gladwell talent narcissism management structure business date: 11-19-2009 06:02 gmt revision:1 [0] [head]

http://www.gladwell.com/pdf/talent.pdf -- From 2002. Old but excellent. Structure is required to achieve broad, slow to ROI projects. (It's almost common sense when expressed this way!)

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ref: work-0 tags: machine learning reinforcement genetic algorithms date: 10-26-2009 04:49 gmt revision:1 [0] [head]

I just had dinner with Jesse, and the we had a good/productive discussion/brainstorm about algorithms, learning, and neurobio. Two things worth repeating, one simpler than the other:

1. Gradient descent / Newton-Rhapson like techniques should be tried with genetic algorithms. As of my current understanding, genetic algorithms perform an semi-directed search, randomly exploring the space of solutions with natural selection exerting a pressure to improve. What if you took the partial derivative of each of the organism's genes, and used that to direct mutation, rather than random selection of the mutated element? What if you looked before mating and crossover? Seems like this would speed up the algorithm greatly (though it might get it stuck in local minima, too). Not sure if this has been done before - if it has, edit this to indicate where!

2. Most supervised machine learning algorithms seem to rely on one single, externally applied objective function which they then attempt to optimize. (Rather this is what convex programming is. Unsupervised learning of course exists, like PCA, ICA, and other means of learning correlative structure) There are a great many ways to do optimization, but all are exactly that - optimization, search through a space for some set of weights / set of rules / decision tree that maximizes or minimizes an objective function. What Jesse and I have arrived at is that there is no real utility function in the world, (Corollary #1: life is not an optimization problem (**)) -- we generate these utility functions, just as we generate our own behavior. What would happen if an algorithm iteratively estimated, checked, cross-validated its utility function based on the small rewards actually found in the world / its synthetic environment? Would we get generative behavior greater than the complexity of the inputs? (Jesse and I also had an in-depth talk about information generation / destruction in non-linear systems.)

Put another way, perhaps part of learning is to structure internal valuation / utility functions to set up reinforcement learning problems where the reinforcement signal comes according to satisfaction of sub-goals (= local utility functions). Or, the gradient signal comes by evaluating partial derivatives of actions wrt Creating these goals is natural but not always easy, which is why one reason (of very many!) sports are so great - the utility function is clean, external, and immutable. The recursive, introspective creation of valuation / utility functions is what drives a lot of my internal monologues, mixed with a hefty dose of taking partial derivatives (see {780}) based on models of the world. (Stated this way, they seem so similar that perhaps they are the same thing?)

To my limited knowledge, there has been some work as of recent in the creation of sub-goals in reinforcement learning. One paper I read used a system to look for states that had a high ratio of ultimately rewarded paths to unrewarded paths, and selected these as subgoals (e.g. rewarded the agent when this state was reached.) I'm not talking about these sorts of sub-goals. In these systems, there is an ultimate goal that the researcher wants the agent to achieve, and it is the algorithm's (or s') task to make a policy for generating/selecting behavior. Rather, I'm interested in even more unstructured tasks - make a utility function, and a behavioral policy, based on small continuous (possibly irrelevant?) rewards in the environment.

Why would I want to do this? The pet project I have in mind is a 'cognitive' PCB part placement / layout / routing algorithm to add to my pet project, kicadocaml, to finally get some people to use it (the attention economy :-) In the course of thinking about how to do this, I've realized that a substantial problem is simply determining what board layouts are good, and what are not. I have a rough aesthetic idea + some heuristics that I learned from my dad + some heuristics I've learned through practice of what is good layout and what is not - but, how to code these up? And what if these aren't the best rules, anyway? If i just code up the rules I've internalized as utility functions, then the board layout will be pretty much as I do it - boring!

Well, I've stated my sub-goal in the form of a problem statement and some criteria to meet. Now, to go and search for a decent solution to it. (Have to keep this blog m8ta!) (Or, realistically, to go back and see if the problem statement is sensible).

(**) Corollary #2 - There is no god. nod, Dawkins.

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ref: work-0 tags: emergent leabra QT neural networks GUI interface date: 10-21-2009 19:02 gmt revision:4 [3] [2] [1] [0] [head]

I've been reading Computational Explorations in Cognitive Neuroscience, and decided to try the code that comes with / is associated with the book. This used to be called "PDP+", but was re-written, and is now called Emergent. It's a rather large program - links to Qt, GSL, Coin3D, Quarter, Open Dynamics Library, and others. The GUI itself seems obtuse and too heavy; it's not clear why they need to make this so customized / panneled / tabbed. Also, it depends on relatively recent versions of each of these libraries - which made the install on my Debian Lenny system a bit of a chore (kinda like windows).

A really strange thing is that programs are stored in tree lists - woah - a natural folding editor built in! I've never seen a programming language that doesn't rely on simple text files. Not a bad idea, but still foreign to me. (But I guess programs are inherently hierarchal anyway.)

Below, a screenshot of the whole program - note they use a Coin3D window to graph things / interact with the model. The colored boxes in each network layer indicate local activations, and they update as the network is trained. I don't mind this interface, but again it seems a bit too 'heavy' for things that are inherently 2D (like 2D network activations and the output plot). It's good for seeing hierarchies, though, like the network model.

All in all looks like something that could be more easily accomplished with some python (or ocaml), where the language itself is used for customization, and not a GUI. With this approach, you spend more time learning about how networks work, and less time programming GUIs. On the other hand, if you use this program for teaching, the gui is essential for debugging your neural networks, or other people use it a lot, maybe then it is worth it ...

In any case, the book is very good. I've learned about GeneRec, which uses different activation phases to compute local errors for the purposes of error-minimization, as well as the virtues of using both Hebbian and error-based learning (like GeneRec). Specifically, the authors show that error-based learning can be rather 'lazy', purely moving down the error gradient, whereas Hebbian learning can internalize some of the correlational structure of the input space. You can look at this internalization as 'weight constraint' which limits the space that error-based learning has to search. Cool idea! Inhibition also is a constraint - one which constrains the network to be sparse.

To use his/their own words:

... given the explanation above about the network's poor generalization, it should be clear why both Hebbian learning and kWTA (k winner take all) inhibitory competition can improve generalization performance. At the most general level, they constitute additional biases that place important constraints on the learning and the development of representations. Mroe specifically, Hebbian learning constrains the weights to represent the correlational structure of the inputs to a given unit, producing systematic weight patterns (e.g. cleanly separated clusters of strong correlations).

Inhibitory competition helps in two ways. First, it encourages individual units to specialize in representing a subset of items, thus parcelling up the task in a much cleaner and more systematic way than would occur in an otherwise unconstrained network. Second, inhibition greatly restricts the settling dynamics of the network, greatly constraining the number of states the network can settle into, and thus eliminating a large proportion of the attractors that can hijack generalization.."

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ref: -0 tags: live origin biochemistry ocean vents date: 10-21-2009 02:23 gmt revision:0 [head]

Our ancestor, a proton powered rock?-- great article, wish i knew more of the biochemistry behind this research.

linked from that, something of a less pure science:

Future women, shorter, plumper, more fertile --read the comments, some of them are insane (but provocative?)! Viz:

  • I think there's a growing conscious awareness among men that tall, (generally) intelligent women are actually not good catches despite their good looks. They're prone to psychosexual neurosis, are high-maintenance, competitive, self-centered and often simply don't want to have the man's children. Much of that is socially indoctrinated behavior (in North America) but there is probably a genetic substrate underlying it.
  • Lombroso found in the late nineteenth century (modern criminologists tend to dispute some of his methodology) that promiscuous women can also be distinguished by a large gap between the big toe and next adjacent toe.

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ref: Darmanjian-2006.01 tags: wireless neural recording university Florida Principe telemetry msp430 dsp nordic date: 04-15-2009 20:56 gmt revision:1 [0] [head]

PMID-17946962[0] A reconfigurable neural signal processor (NSP) for brain machine interfaces.

  • use a Texas instruments TMS320VC33 200MFLOPS (yes floating point) DSP,
  • a nordic NRF24L01,
  • a MSP430F1611x as a co-processor / wireless protocol manager / bootloader,
  • an Altera EPM3128ATC100 CPLD for expansion / connection.
  • uses 450 - 600mW in use (running an LMS algorithm).


[0] Darmanjian S, Cieslewski G, Morrison S, Dang B, Gugel K, Principe J, A reconfigurable neural signal processor (NSP) for brain machine interfaces.Conf Proc IEEE Eng Med Biol Soc 1no Issue 2502-5 (2006)

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ref: Legenstein-2008.1 tags: Maass STDP reinforcement learning biofeedback Fetz synapse date: 04-09-2009 17:13 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-18846203[0] A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

  • (from abstract) The resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP.
    • This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker.
  • STDP is prevalent in the cortex ; however, it requires a second signal:
    • Dopamine seems to gate STDP in corticostriatal synapses
    • ACh does the same or similar in the cortex. -- see references 8-12
  • simple learning rule they use: d/dtW ij(t)=C ij(t)D(t) d/dt W_{ij}(t) = C_{ij}(t) D(t)
  • Their notes on the Fetz/Baker experiments: "Adjacent neurons tended to change their firing rate in the same direction, but also differential changes of directions of firing rates of pairs of neurons are reported in [17] (when these differential changes were rewarded). For example, it was shown in Figure 9 of [17] (see also Figure 1 in [19]) that pairs of neurons that were separated by no more than a few hundred microns could be independently trained to increase or decrease their firing rates."
  • Their result is actually really simple - there is no 'control' or biofeedback - there is no visual or sensory input, no real computation by the network (at least for this simulation). One neuron is simply reinforced, hence it's firing rate increases.
    • Fetz & later Schimdt's work involved feedback and precise control of firing rate; this does not.
    • This also does not address the problem that their rule may allow other synapses to forget during reinforcement.
  • They do show that exact spike times can be rewarded, which is kinda interesting ... kinda.
  • Tried a pattern classification task where all of the information was in the relative spike timings.
    • Had to run the pattern through the network 1000 times. That's a bit unrealistic (?).
      • The problem with all these algorithms is that they require so many presentations for gradient descent (or similar) to work, whereas biological systems can and do learn after one or a few presentations.
  • Next tried to train neurons to classify spoken input
    • Audio stimului was processed through a cochlear model
    • Maass previously has been able to train a network to perform speaker-independent classification.
    • Neuron model does, roughly, seem to discriminate between "one" and "two"... after 2000 trials (each with a presentation of 10 of the same digit utterance). I'm still not all that impressed. Feels like gradient descent / linear regression as per the original LSM.
  • A great many derivations in the Methods section... too much to follow.
  • Should read refs:
    • PMID-16907616[1] Gradient learning in spiking neural networks by dynamic perturbation of conductances.
    • PMID-17220510[2] Solving the distal reward problem through linkage of STDP and dopamine signaling.


[0] Legenstein R, Pecevski D, Maass W, A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback.PLoS Comput Biol 4:10, e1000180 (2008 Oct)
[1] Fiete IR, Seung HS, Gradient learning in spiking neural networks by dynamic perturbation of conductances.Phys Rev Lett 97:4, 048104 (2006 Jul 28)
[2] Izhikevich EM, Solving the distal reward problem through linkage of STDP and dopamine signaling.Cereb Cortex 17:10, 2443-52 (2007 Oct)

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ref: Oskoei-2008.08 tags: EMG pattern analysis classification neural network date: 04-07-2009 21:10 gmt revision:2 [1] [0] [head]

  • EMG pattern analysis and classification by Neural Network
    • 1989!
    • short, simple paper. showed that 20 patterns can accurately be decoded with a backprop-trained neural network.
  • PMID-18632358 Support vector machine-based classification scheme for myoelectric control applied to upper limb.
    • myoelectric discrimination with SVM running on features in both the time and frequency domain.
    • a survace MES (myoelectric sensor) is formed via the superposition of individual action potentials generated by irregular discharges of active motor units in a muscle fiber. It's amplitude, variance, energy, and frequency vary depending on contration level.
    • Time domain features:
      • Mean absolute value (MAV)
      • root mean square (RMS)
      • waveform length (WL)
      • variance
      • zero crossings (ZC)
      • slope sign changes (SSC)
      • William amplitude.
    • Frequency domain features:
      • power spectrum
      • autoregressive coefficients order 2 and 6
      • mean signal frequency
      • median signal frequency
      • good performance with just RMS + AR2 for 50 or 100ms segments. Used a SVM with a RBF kernel.
      • looks like you can just get away with time-domain metrics!!

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ref: Huber-2004.07 tags: sleep REM SWS wilson synaptic strength date: 04-01-2009 17:50 gmt revision:2 [1] [0] [head]

http://www.the-scientist.com/2009/04/1/34/1/ -- good layperson-level review of the present research on sleep. Includes interviews with Strickgold and other prominents. References:

http://www.the-scientist.com/2009/04/1/15/1/ -- points out that Western sleep style is a relative outlier compared to sleeping in other cultures. More 'primitive' cultures have polyphasic sleep, with different stages of alertness, dozing, napping, disengaged, vigilance, etc.

  • Quote: Other cultures tend towards "multiple and multiage sleeping partners; frequent proximity of animals; embeddedness of sleep in ongoing social interaction; fluid bedtimes and wake times; use of nighttime for ritual, sociality, and information exchange; and relatively exposed sleeping locations that require fire maintenance and sustained vigilance."


[0] Huber R, Ghilardi MF, Massimini M, Tononi G, Local sleep and learning.Nature 430:6995, 78-81 (2004 Jul 1)
[1] Klintsova AY, Greenough WT, Synaptic plasticity in cortical systems.Curr Opin Neurobiol 9:2, 203-8 (1999 Apr)
[2] Vyazovskiy VV, Cirelli C, Pfister-Genskow M, Faraguna U, Tononi G, Molecular and electrophysiological evidence for net synaptic potentiation in wake and depression in sleep.Nat Neurosci 11:2, 200-8 (2008 Feb)
[3] Pavlides C, Winson J, Influences of hippocampal place cell firing in the awake state on the activity of these cells during subsequent sleep episodes.J Neurosci 9:8, 2907-18 (1989 Aug)
[4] Pompeiano M, Cirelli C, Arrighi P, Tononi G, c-Fos expression during wakefulness and sleep.Neurophysiol Clin 25:6, 329-41 (1995)
[5] Hill S, Tononi G, Modeling sleep and wakefulness in the thalamocortical system.J Neurophysiol 93:3, 1671-98 (2005 Mar)
[6] Aton SJ, Seibt J, Dumoulin M, Jha SK, Steinmetz N, Coleman T, Naidoo N, Frank MG, Mechanisms of sleep-dependent consolidation of cortical plasticity.Neuron 61:3, 454-66 (2009 Feb 12)

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ref: Ribeiro-2004.12 tags: Sidarta Ribeiro reverberation sleep consolidation integration replay REM SWS date: 03-26-2009 03:19 gmt revision:2 [1] [0] [head]

PMID-15576886[0] Reverberation, storage, and postsynaptic propagation of memories during sleep

  • Many references in the first paragraph! They should switch to the [n] notation; the names are disruptive.
  • Show reverberation (is this measured in a scale-invariant way?) increases after novel object is placed in cage. Recorded from a single rat for up to 96 hours.
  • also looked at Zif-268 activation in the cortex (autoradiogram);
    • Previous results showed that Zif-268 levels are up-regulated in REM but not SWS in the hippocampus and cerebral cortex of exposed animals. (Ribeiro 1999)
    • hippocampal inactivation during REM sleep blocked zif-268 upregulation.
    • quote: "Increased activity is necessary but not sufficient to induce zif-268 expression, which also requires calcium inflow via NMDA channels and phosphorilation of the cAMP response element-binding protein (CREB)"
  • Sleep deprivation is much more detrimental to implicit than to explicit memory consolidation (Fowler et al. 1973; Karni et al. 1994; Smith 1995, 2001; Stickgold et al. 2000a; Laureys et al. 2002; Walker et al. 2002; Maquet et al. 2003; Mednick et al. 2003)


[0] Ribeiro S, Nicolelis MA, Reverberation, storage, and postsynaptic propagation of memories during sleep.Learn Mem 11:6, 686-96 (2004 Nov-Dec)

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ref: Shadmehr-1997.01 tags: Shadmehr human long term memory learning motor M1 cortex date: 03-25-2009 15:29 gmt revision:2 [1] [0] [head]

PMID-8987766[0] Functional Stages in the Formation of Human Long-Term Motor Memory

  • We demonstrate that two motor maps may be learned and retained, but only if the training sessions in the tasks are separated by an interval of ~5 hr.
  • Analysis of the after-effects suggests that with a short temporal distance, learning of the second task leads to an unlearning of the internal model for the first.
  • many many citations!


[0] Shadmehr R, Brashers-Krug T, Functional stages in the formation of human long-term motor memory.J Neurosci 17:1, 409-19 (1997 Jan 1)

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ref: Graves-2001.04 tags: sleep memory REM protein synthesis review date: 03-25-2009 15:23 gmt revision:1 [0] [head]

PMID-11250009[0] Sleep and memory: a molecular perspective.

  • inhibition of protein synthesis is most effective if it occurs at a time post-training when rapid eye movement (REM) sleep is required for memory consolidation
  • The neurochemical changes that occur across sleep/wake states, especially the cholinergic changes that occur in the hippocampus during REM sleep, might provide a mechanism by which sleep modulates specific cellular signaling pathways involved in hippocampus-dependent memory storage.
    • REM sleep could influence the consolidation of hippocampus-dependent long-term memory if it occurs during windows that are sensitive to cholinergic or serotonergic signaling.
    • PKA activation seems important to hippocampal long-term memory
    • NMDA affects PKA through Ca2+ to adenyl cyclase
    • 5-HT_1A receptor negatively coupled to adenyl cyclase (AC)
    • 5-HT concentrations go down in hippocampus during sleep ?


[0] Graves L, Pack A, Abel T, Sleep and memory: a molecular perspective.Trends Neurosci 24:4, 237-43 (2001 Apr)

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ref: MAPlle-2009.03 tags: sleep spindles learning ripples LFP hippocampus neocortex synchrony SWS REM date: 03-25-2009 15:05 gmt revision:2 [1] [0] [head]

PMID-19245368[0] The influence of learning on sleep slow oscillations and associated spindles and ripples in humans and rats

  • Here we examined whether slow oscillations also group learning-induced increases in spindle and ripple activity, thereby providing time-frames of facilitated hippocampus-to-neocortical information transfer underlying the conversion of temporary into long-term memories.
  • No apparent grouping effect between slow oscillations and learning-induced spindles and ripples in rats.
  • Stronger effect of learning on spindles (neocortex) and ripples (hippocampus) ; less or little effect of learning on slow waves in the neocortex.
  • have a good plot showing their time-series analysis:


[0] Mölle M, Eschenko O, Gais S, Sara SJ, Born J, The influence of learning on sleep slow oscillations and associated spindles and ripples in humans and rats.Eur J Neurosci 29:5, 1071-81 (2009 Mar)

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ref: Rasch-2009.04 tags: REM learning procedural memory sleep spindles date: 03-23-2009 18:32 gmt revision:3 [2] [1] [0] [head]

PMID-18836440[0] Pharmacological REM sleep suppression paradoxically improves rather than impairs skill memory

  • surpressed REM sleep with SSRIs or norepinephrine reuptake inhibitor
    • yet tested the subjects after a long wash-out: 32 hours, including 2 nights sleep.
  • did not impair word-pair recognition, and improved finger tapping accuracy.
  • sleep spindles are a feature of non-REM sleep.
  • REM sleep is characterized by an abscence of serotonin and norepinephrine; SSRIs and SNRIs increase the levels of these two neurotransmitters, respectively, at the synaptic cleft.
  • clinical studies of depressed patients show no impairment of skill performance during long-term treatment with these drugs, despite marked REM supression
  • did mirror-tracing and finger-tapping tasks.
  • SSRI supressed REM sleep; SNRI almost completely removed REM.
  • treatment increased accuracy of finger tapping task! esp. for the SNRI.
    • increase in accuracy was positively correlated to the change in spindle density.
  • For the mirror task, there were notable improvements after sleep, but no significant difference between placebo, SSRI, and SNRI groups.
  • paired-word retention task has been shown dependent on SWS; it was not affected by pharmacology.
  • They suggest that perhaps SSRI /SNRI supressed simply the typical measures of REM sleep, and that other factors critical for the associated consolidation were unaffected (e.g. high cholinergic activity).
  • result is consistent with [1]


[0] Rasch B, Pommer J, Diekelmann S, Born J, Pharmacological REM sleep suppression paradoxically improves rather than impairs skill memory.Nat Neurosci no Volume no Issue no Pages (2008 Oct 5)
[1] Tamaki M, Matsuoka T, Nittono H, Hori T, Fast sleep spindle (13-15 hz) activity correlates with sleep-dependent improvement in visuomotor performance.Sleep 31:2, 204-11 (2008 Feb 1)

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ref: Eusebio-2009.05 tags: DBS STN beta gamma oscillations synchrony tremor review date: 03-23-2009 18:32 gmt revision:1 [0] [head]

PMID-19233172[0] Synchronisation in the beta frequency-band - The bad boy of parkinsonism or an innocent bystander?

  • Excessive synchronisation of basal ganglia neuronal activity in the beta frequency band has been implicated in Parkinson's disease
  • However, the extent to which beta synchrony has a mechanistic (rather than epiphenomenal) role in parkinsonism remains unclear, and the suppression of this activity by deep brain stimulation is contentious.
PMID-16289053[1] Intra-operative STN DBS attenuates the prominent beta rhythm in the STN in Parkinson's disease.
  • Beta rhythm for them = 11-30Hz. Observed in the LFP recorded from the DBS electrode itself.
  • This study shows for the first time that STN DBS attenuates the power in the prominent beta band recorded in the STN of patients with PD.


[0] Eusebio A, Brown P, Synchronisation in the beta frequency-band - The bad boy of parkinsonism or an innocent bystander?Exp Neurol no Volume no Issue no Pages (2009 Feb 20)
[1] Wingeier B, Tcheng T, Koop MM, Hill BC, Heit G, Bronte-Stewart HM, Intra-operative STN DBS attenuates the prominent beta rhythm in the STN in Parkinson's disease.Exp Neurol 197:1, 244-51 (2006 Jan)

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ref: Maquet-2001.11 tags: sleep learning memory Maquet date: 03-20-2009 18:38 gmt revision:1 [0] [head]

PMID-11691982[0] The Role of Sleep in Learning and Memory

  • 8 years ago; presumably much has changed?
  • NREM = SWS; REM = PS (paradoxical sleep)
  • nice table in there! looks as though he was careful in background research on this one; plenty of references.
  • "indeed, stress can also lead to an increase in REM sleep." -- but this may only be related to the presence of new material.
    • however, there is no increase in REM sleep if there is no material to learn.
  • reminder that theta rhythm is seen in the hippocampus in both exploratory activity and in REM sleep.
    • anticipated the presence of replay in the hippocampus
  • spindles allow the entry of Ca+2, which facilitates LTP (?).
  • I should check up on songbird learning (mentioned in the review!).
    • Young zebra finches have to establish the correspondence between vocal production (motor output) and the resulting auditory feedback (sensory).
    • This cannot be done during waking because the bird song arises from tightly time-coded sequence of activity; during sleep, however, motor output can be compared to sensory feedback (so as to capture an inverse model?)
  • PGO (ponto-geniculo-occipital) waves occur immediately before REM sleep. PGO waves are more common in rats after aversive training.
  • ACh increases cortical plasticity in adult mammals; REM sleep is characterized by a high level of ACh and 5-HT (serotonin).
  • sleep may not be necessary for recall-based learning, it just may be a goot time for it. Sharp waves and ripples are observed in both quiet waking and SWS.
  • Learning to reach in a force field is consolidated in 5 hours after training. [1]
  • Again mentions the fact that antidipressant drugs, which drastically reduce the amount of REM sleep, do not aversely affect memory.


[0] Maquet P, The role of sleep in learning and memory.Science 294:5544, 1048-52 (2001 Nov 2)
[1] Shadmehr R, Brashers-Krug T, Functional stages in the formation of human long-term motor memory.J Neurosci 17:1, 409-19 (1997 Jan 1)

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ref: Stickgold-2001.11 tags: review dream sleep REM NREM SWS learning memory replay date: 03-19-2009 17:09 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

PMID-11691983[0] Sleep, Learning, and Dreams: Off-line Memory Reprocessing

  • sleep can be broadly divided into REM (rapid eye movement) and NREM (no rapid eye movement) sleep, with the REM-NREM cycle lasting 90 minutes in humans.
  • REM seems involved in proper binocular wiring in the visual cortex, development of problem solving skills, and discrimination tasks.
    • REM sleep seems as important as visual experience for wiring binocular vision.
  • REM seems critical for learning procedural memories, but not declarative (by the authors claim that the tasks used in declarative tests are too simple).
    • Depriving rats of REM sleep can impair procedural learning at test points up to a week later.
    • SWS may be better for consolidation of declarative memory.
  • Strongest evidence comes from a visual texture discrimination task, where improvements are only seen after REM sleep.
    • REM has also been shown to have an effect in learning of complex logic games, foreign language acquisition, and after intensive studying.
    • Solving anagrames stronger after being woken up from REM sleep. (!)
  • REM (hypothetically) involves NC -> hippocampus; SWS involves hippocampus -> NC (hence declarative memory). (Buzaki 1996).
    • This may use theta waves, which enhance LTP in the hippocampus; the slow large depolarizations in SWS may facilitate LTP in the cortex.
  • Replay in the rat hippocampus:
    • replay occurs within layer CA1 during SWS for a half hour or so after learning, and in REM after 24 hours.
    • replay shifts from being in-phase with the theta wave activity (e.g. helping LTP) to being out of phase (coinicident with troughs, possibly used to 'erase' memories from the hippocampus?); this is in accord with memories becoming hippocampally independent.
  • ACh levels are at waking levels or higher, and levels of NE (noradrenergic) & 5-HT go near zero.
  • DLPFC (dorsolateral prefrontal cortex) is inhibited during REM sleep - presumably, this results in an inability to allocate attentional resources.
  • ACC (anterior cingulate cortex), MFC (medial frontal cortex), and the amygdala are highly active in REM sleep.
  • if you block correlates of learning - PKA pathwat, zif-268 genes during REM, learning is impaired.
  • In the context of a multilevel system of sleep-dependent memory reprocessing, dreams represent the conscious awareness of complex brain systems involved in the reprocessing of emotions and memories during sleep.
    • the whole section on dreaming is really interesting!


[0] Stickgold R, Hobson JA, Fosse R, Fosse M, Sleep, learning, and dreams: off-line memory reprocessing.Science 294:5544, 1052-7 (2001 Nov 2)

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ref: notes-0 tags: wireless nordic headstage bridge neurorecord pictures photo EMG myopen date: 03-12-2009 02:33 gmt revision:4 [3] [2] [1] [0] [head]

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ref: Cooke-1990.03 tags: motor organization triphasic control EMG date: 03-11-2009 21:42 gmt revision:12 [11] [10] [9] [8] [7] [6] [head]

the organization of the human triphasic EMG control sequence:

  • PMID-2329356[0] Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.
  • PMID-8989378[1]
  • PMID-1629754[2]
  • PMID-2230915[3]
  • PMID-2329365[4]
  • PMID-2769335[5]
  • PMID-2769334[6]
  • PMID-3622686[7] Trajectory control in targeted force impulses. I. Role of opposing muscles.
    • Our findings emphasize that neuronal commands to opposing muscles acting at a joint must be adapted to constraints imposed by the properties of the neuromuscular plant.
  • PMID-10085332[8] Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.
    • frictionless air-jet system, rapid movements, inertia perturbation via masses on the joints, surprise trials.
    • surprise trials were well predicted by an open-loop feedforward controller.
    • there was feedback compensation upon return-to-center: it is not all feedforward (of course!)


[0] Cooke JD, Brown SH, Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.J Neurophysiol 63:3, 465-72 (1990 Mar)[1] Almeida GL, Hong DA, Corcos D, Gottlieb GL, Organizing principles for voluntary movement: extending single-joint rules.J Neurophysiol 74:4, 1374-81 (1995 Oct)[2] Gottlieb GL, Latash ML, Corcos DM, Liubinskas TJ, Agarwal GC, Organizing principles for single joint movements: V. Agonist-antagonist interactions.J Neurophysiol 67:6, 1417-27 (1992 Jun)[3] Corcos DM, Agarwal GC, Flaherty BP, Gottlieb GL, Organizing principles for single-joint movements. IV. Implications for isometric contractions.J Neurophysiol 64:3, 1033-42 (1990 Sep)[4] Gottlieb GL, Corcos DM, Agarwal GC, Latash ML, Organizing principles for single joint movements. III. Speed-insensitive strategy as a default.J Neurophysiol 63:3, 625-36 (1990 Mar)[5] Corcos DM, Gottlieb GL, Agarwal GC, Organizing principles for single-joint movements. II. A speed-sensitive strategy.J Neurophysiol 62:2, 358-68 (1989 Aug)[6] Gottlieb GL, Corcos DM, Agarwal GC, Organizing principles for single-joint movements. I. A speed-insensitive strategy.J Neurophysiol 62:2, 342-57 (1989 Aug)[7] Ghez C, Gordon J, Trajectory control in targeted force impulses. I. Role of opposing muscles.Exp Brain Res 67:2, 225-40 (1987)[8] Sainburg RL, Ghez C, Kalakanis D, Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.J Neurophysiol 81:3, 1045-56 (1999 Mar)[9] Seidler RD, Noll DC, Chintalapati P, Bilateral basal ganglia activation associated with sensorimotor adaptation.Exp Brain Res 175:3, 544-55 (2006 Nov)

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ref: Nishida-2007.04 tags: sleep spindle learning nap NREM date: 03-06-2009 17:56 gmt revision:1 [0] [head]

PMID-17406665[0] Daytime naps, motor memory consolidation and regionally specific sleep spindles.

  • asked subjects to learn a motor task with their non-dominant hand, and then tested them 8 hours later.
  • subjects that were allowed a 60-90 minute siesta improved their performance significantly relative to controls and relative to previous performance.
  • when they subtracted EEG activity of the non-learning hemisphere from the learning hemisphere, spindle activity was strongly correlated with offline memory improvement.


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ref: KAli-2004.03 tags: hippocampus memory model Dayan replay learning memory date: 03-06-2009 17:53 gmt revision:1 [0] [head]

PMID-14983183[0] Off-line replay maintains declarative memories in a model of hippocampal-neocortical interactions

  • (i'm skimming the article)
  • The neocortex acts as a probabilistic generative model. unsupervised learning extracts categories, tendencies and correlations from the statistics of the inputs into the [synaptic weights].
  • Their hypothesis is that hippocampal replay is required for maintenance of episodic memories; their model and simulations support this.
  • quote: "However, the computational goal of episodic learning is storing individual events rather than discovering statistical structure, seemingly rendering consolidation inappropriate. If initial hippocampal storage of the episode already ensures that it can later be recalled episodically, then, barring practical advantages such as storage capacity (or perhaps efficiency), there seems little point in duplicating this capacity in neocortex." makes sense!


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ref: Foster-2006.03 tags: hippocampus memory place cells reverse replay Wilson date: 03-06-2009 17:53 gmt revision:1 [0] [head]

PMID-16474382[0] Reverse replay of behavioral sequences in hippocampal place cells during the awake state.

  • wow: they show compressed reverse replay of firing sequences of hippocampal place cells during movement. While the rat is awake, too!
  • recorded up to 128 cells from the rat hippocampus; 4 animals.
  • the replay occurred while the rat was stopped, and lasted a few hundred milliseconds (~300).
  • phenomena appears to be very common, at least for the rats on the novel tracks.
  • replay events were coincident with ripples in the hippocampal EEG, which also occurs during sleep.
    • however, during slow-wave sleep, the replay was forward.
  • they offer a reasonable hypothesis for the reverse replay's function: it is used to propagate value information from the rewarded lcoation backwards along incoming (behavioral) trajectories.
    • quote "awake replay represents efficient use of hard-won experience."


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ref: Tamaki-2008.02 tags: sleep spindle NREM motor learning date: 02-18-2009 17:44 gmt revision:0 [head]

PMID-18274267[0] Fast sleep spindle (13-15 hz) activity correlates with sleep-dependent improvement in visuomotor performance.

  • mirror-tracing task performance improves following a night's sleep.
  • the improvement is correlated with the fast-spindle activity.
  • spindles were detected from EEG recordings with a 10-16hz butterworth filter in matlab. Spindles had to be >= 15uv, >= 0.5s
    • slow spindles = 10-13Hz, predominant in the frontal regions.
    • fast spindles > 13hz, predominant in the parietal regions.


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ref: Morin-2008.08 tags: sleep spindles NREM motor learning date: 02-18-2009 17:35 gmt revision:2 [1] [0] [head]

PMID-18714787[0] Motor sequence learning increases sleep spindles and fast frequencies in post-training sleep.

  • as you can read in the title, it is the motor learning that increases the spindles. They did not look for causality in the opposite direction.
  • Task was finger-tap motor sequence learning, with control. Subjects had to type on a computer keyboard using the nondominant hand. No visual feedback was given during non-training performance (e.g. during practice).
  • Beta-frequencies are greater in sleep after motor learning. , though this is not correlated with actual consolidation.
  • Other studies have shown that spindles are also more frequent after spatial or verbal learning.
  • observed no effect of SWS on motor sequence learning.


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ref: Rasch-2009.06 tags: sleep cholinergic acetylcholine REM motor consolidation date: 02-18-2009 17:27 gmt revision:0 [head]

PMID-19194375[0] "Impaired Off-Line Consolidation of Motor Memories After Combined Blockade of Cholinergic Receptors During REM Sleep-Rich Sleep."

  • In REM sleep there is high, almost to wake-like, levels of ACh activity (in the cortex? they don't say).
  • Trained subjects on a motor task after a 3-hour period of slow wave sleep.
  • Then administered ACh (muscarinic + nicotinic) blockers or placebo
  • Subjects with blocked ACh reception showed less motor consolidation. So, ACh is needed! (This is consistent with Ach being an attentional / selective signal for activating the cortex).


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ref: Peters-2008.05 tags: Schaal reinforcement learning policy gradient motor primitives date: 02-17-2009 18:49 gmt revision:4 [3] [2] [1] [0] [head]

PMID-18482830[0] Reinforcement learning of motor skills with policy gradients

  • they say that the only way to deal with reinforcement or general-type learning in a high-dimensional policy space defined by parameterized motor primitives are policy gradient methods.
  • article is rather difficult to follow; they do not always provide enough details (for me) to understand exactly what their equations mean. Perhaps this is related to their criticism that others's papers are 'ad-hoc' and not 'statistically motivated'
  • none the less, it seems interesting..
  • their previous paper - Reinforcement learning for Humanoid robotics - maybe slightly easier to understand.


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ref: notes-0 tags: Barto Hierarchal Reinforcement Learning date: 02-17-2009 05:38 gmt revision:1 [0] [head]

Recent Advancements in Hierarchal Reinforcement Learning

  • RL with good function-approximation methods for evaluating the value function or policy function solve many problems yet...
  • RL is bedeviled by the curse of dimensionality: the number of parameters grows exponentially with the size of a compact encoding of state.
  • Recent research has tackled the problem by exploiting temporal abstraction - decisions are not required at each step, but rather invoke the activity of temporally extended sub-policies. This is somewhat similar to a macro or subroutine in programming.
  • This is fundamentally similar to adding detailed domain-specific knowledge to the controller / policy.
  • Ron Parr seems to have made significant advances in this field with 'hierarchies of abstract machines'.
    • I'm still looking for a cognitive (predictive) extension to these RL methods ... these all are about extension through programmer knowledge.
  • They also talk about concurrent RL, where agents can pursue multiple actions (or options) at the same time, and assess value of each upon completion.
  • Next are partially observable markov decision processes, where you have to estimate the present state (belief state), as well as a policy. It is known that and optimal solution to this task is intractable. They propose using Hierarchal suffix memory as a solution ; I can't really see what these are about.
    • It is also possible to attack the problem using hierarchal POMDPs, which break the task into higher and lower level 'tasks'. Little mention is given to the even harder problem of breaking sequences up into tasks.
  • Good review altogether, reasonable balance between depth and length.

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ref: Pearlmutter-2009.06 tags: sleep network stability learning memory date: 02-05-2009 19:21 gmt revision:1 [0] [head]

PMID-19191602 A New Hypothesis for Sleep: Tuning for Criticality.

  • Their hypothesis: in the course of learning, the brain's networks move closer to instability, as the process of learning and information storage requires that the network move closer to instability.
    • That is, a perfectly stable network stores no information: output is the same independent of input; a highly unstable network can potentially store a lot of information, or be a very selective or critical system: output is highly sensitive to input.
  • Sleep serves to restore the stability of the network by exposing it to a variety of inputs, checking for runaway activity, and adjusting accordingly. (inhibition / glia? how?)
  • Say that when sleep is not possible, an emergency mechanism must com into play, namely tiredness, to prevent runaway behavior.
  • (From wikipedia:) a potentially serious side-effect of many antipsychotics is that they tend to lower a individual's seizure threshold. Recall that removal of all dopamine can inhibit REM sleep; it's all somehow consistent, but unclear how maintaining network stability and being able to move are related.

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ref: Kakade-2002.07 tags: dopamine reward reinforcement learning Kakade Dayan date: 12-09-2008 21:27 gmt revision:1 [0] [head]

PMID-12371511[0] Dopamine: generalization and bonuses

  • suggest that some anomalies of dopamine activity is related to generalization and novelty. In terms of novelty, dopamine may be shaping exploration.
  • review results that DA activity signal a global prediction error for summed future reward in conditioning tasks.
    • above, A = pre-training; B = post-training; C = catch trial.
    • this type of model is essentially TD(0); it does not involve 'eligibility traces', but still is capable of learning.
    • remind us that these cells have been found, but there are many other different types of responses of dopmamine cells.
  • storage of these predictions involves the basolateral nuclei of the amygdala and the orbitofrontal cortex. (but how do these structures learn their expectations ... ?)
  • dopamine release is associated with motor effects that are species specific, like approach behaviors, that can be irrelevant or detrimental to the delivery of reward.
  • bonuses, for the authors = fictitious quantities added to rewards or values to ensure appropriate exploration.
  • resolution of DA activity ~ 50ms.
  • Romo & Schultz have found that there are phasic increases in DA activity to both rewarded and non-rewarded events/stimuli - something that they explain as 'generalization'. But - maybe it is something else? like a startle / get ready to move response?
  • They suggest that it is a matter of intermediate states where the monkey is uncertain as to what to do / what will happen. hum, not sure about this.


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ref: notes-0 tags: policy gradient reinforcement learning aibo walk optimization date: 12-09-2008 17:46 gmt revision:0 [head]

Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion

  • simple, easy to understand policy gradient method! many papers cite this on google scholar.
  • compare to {651}

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ref: Daw-2006.04 tags: reinforcement learning reward dopamine striatum date: 10-07-2008 22:36 gmt revision:1 [0] [head]

PMID-16563737[0] The computational neurobiology of learning and reward

  • I'm sure I read this, but cannot find it in m8ta anymore.
  • short, concise review article.
  • review evidence for actor-critic architectures in the prefrontal cortex.
  • cool: "Perhaps most impressively, a trial-by-trial regression analysis of dopamine responses in a task with varying reward magnitudes showed that the response dependence on the magnitude history has the same form as that expected from TD learning". trial by trial is where it's at! article: Midbrain Dopamine Neurons Encode a Quantitative Reward Prediction Error Signal


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ref: Schultz-2000.12 tags: review reward dopamine VTA basal ganglia reinforcement learning date: 10-07-2008 22:35 gmt revision:1 [0] [head]

PMID-11257908[0] Multiple Reward Signals in the Brain

  • deals with regions in the brain in which reward-related activity has been found, and specifically what the activity looks like.
  • despite the 2000 date, the review feels somewhat dated?
  • similar to [1] except much sorter..


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ref: Schultz-2000.03 tags: review orbitofrontal cortex basal ganglia dopamine reward reinforcement learning striatum date: 10-07-2008 03:53 gmt revision:1 [0] [head]

PMID-10731222[0] Reward processing in primate orbitofrontal cortex and basal ganglia

  • Orbitofrontal neurons showed three principal forms of reward-related activity during the performance of delayed response tasks,
    • responses to reward-predicting instructions,
    • activations during the expectation period immediately preceding reward and
    • responses following reward
    • above, reward-predicting stimulus in a dopamine neuron. Left: the animal received a small quantity of apple juice at irregular intervals without performing in any behavioral task. Right: the animal performed in an operant lever-pressing task in which it released a touch-sensitive resting key and touched a small lever in reaction to an auditory trigger signal. The dopamine neuron lost its response to the primary reward and responded to the reward-predicting sound.
  • for the other figures, read the excellent paper!


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ref: Dzirasa-2006.1 tags: Kafui dopamine sleep REM state-diagram SCLin date: 10-05-2008 17:37 gmt revision:2 [1] [0] [head]

PMID-17035544[0] Dopaminergic control of sleep-wake states


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ref: Graybiel-1994.09 tags: basal ganglia graybeil expert systems motor learning date: 10-03-2008 22:18 gmt revision:2 [1] [0] [head]

PMID-8091209[0] The basal ganglia and adaptive motor control (I couldn't find the pdf for this)

  • the basal ganglia is essentially an expert system which is trained via dopamine.


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ref: Graybiel-2005.12 tags: graybiel motor_learning reinforcement_learning basal ganglia striatum thalamus cortex date: 10-03-2008 17:04 gmt revision:3 [2] [1] [0] [head]

PMID-16271465[] The basal ganglia: Learning new tricks and loving it

  • learning-related changes occur significantly earlier in the striatum than the cortex in a cue-reversal task. she says that this is because the basal ganglia instruct the cortex. I rather think that they select output dimensions from that variance-generator, the cortex.
  • dopamine agonist treatment improves learning with positive reinforcers but not learning with negative reinforcers.
  • there is a strong hyperkinetic pathway that projects directly to the subthalamic nucleus from the motor cortex. this controls output of the inhibitor pathway (GPi)
  • GABA input from the GPi to the thalamus can induce rebound spikes with precise timing. (the outputs are therefore not only inhibitory).
  • striatal neurons have up and down states. recommended action: simultaneous on-line recording of dopamine release and spike activity.
  • interesting generalization: cerebellum = supervised learning, striatum = reinforcement learning. yet yet! the cerebellum has a strong disynaptic projection to the putamen. of course, there is a continuous gradient between fully-supervised and fully-reinforcement models. the question is how to formulate both in a stable loop.
  • striosomal = striatum to the SNc
  • http://en.wikipedia.org/wiki/Substantia_nigra SNc is not an disorganized mass: the dopamergic neurons from the pars compacta project to the cortex in a topological map, dopaminergic neurons of the fringes (the lowest) go to the sensorimotor striatum and the highest to the associative striatum


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ref: Radhakrishnan-2008.1 tags: EMG BMI Jackson motor control learning date: 10-03-2008 16:45 gmt revision:0 [head]

PMID-18667540[0] Learning a novel myoelectric-controlled interface task.

  • EMG-controlled 2D cursor control task with variable output mapping.
  • Subjects could learn non-intuitive output transforms to a high level of performance,
  • Subjects preferred, and learned better, if hand as opposed to arm muscles were used.


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ref: Li-2001.05 tags: Bizzi motor learning force field MIT M1 plasticity memory direction tuning transform date: 09-24-2008 22:49 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-11395017[0] Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field

  • this is concerned with memory cells, cells that 'remember' or remain permanently changed after learning the force-field.
  • In the above figure, the blue lines (or rather vertices of the blue lines) indicate the firing rate during the movement period (and 200ms before); angular position indicates the target of the movement. The force-field in this case was a curl field where force was proportional to velocity.
  • Preferred direction of the motor cortical units changed when the preferred driection of the EMGs changed
  • evidence of encoding of an internal model in the changes in tuning properties of the cells.
    • this can suppor both online performance and motor learning.
    • but what mechanisms allow the motor cortex to change in this way???
  • also see [1]


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ref: Huesler-2000.1 tags: EMG synchronization Hepp-Raymond grip finger force isometric date: 09-07-2008 17:26 gmt revision:3 [2] [1] [0] [head]

PMID-11081826 EMG activation patterns during force production in precision grip. III. Synchronisation of single motor units.

  • synchronization observed in 78% of intrinsic finger muscles (within the hand itself) and 45% of extrinsic finger muscles.
    • force increase was not necessarily correlated to increased synchronization; rather, high synchronization occurred at low force production.
  • instrinsic muscles have higher force sensitivity & higher recruitment thresholds.
  • other articles in the series:
    • PMID-7615027 EMG activation patterns during force production in precision grip. I. Contribution of 15 finger muscles to isometric force.
    • PMID-7615028 EMG activation patterns during force production in precision grip. II. Muscular synergies in the spatial and temporal domain.

Dr. hepp-Raymond himself seems to be a prolific researcher, judging from his pubmed search results. e.g.:

  • PMID-18272868 Absence of gamma-range corticomuscular coherence during dynamic force in a deafferented patient.
    • quote: proprioceptive information is mandatory in the genesis of gamma-band CMC (corticomuscular coherence) during the generation and control of dynamic forces.

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ref: notes-0 tags: linear discriminant analysis LDA EMG date: 07-30-2008 20:56 gmt revision:2 [1] [0] [head]

images/588_1.pdf -- Good lecture on LDA. Below, simple LDA implementation in matlab based on the same:

% data matrix in this case is 36 x 16, 
% with 4 examples of each of 9 classes along the rows, 
% and the axes of the measurement (here the AR coef) 
% along the columns. 
Sw = zeros(16, 16); % within-class scatter covariance matrix. 
means = zeros(9,16); 
for k = 0:8
	m = data(1+k*4:4+k*4, :); % change for different counts / class
	Sw = Sw + cov( m ); % sum the 
	means(k+1, :) = mean( m ); %means of the individual classes
% compute the class-independent transform, 
% e.g. one transform applied to all points
% to project them into one plane. 
Sw = Sw ./ 9; % 9 classes
criterion = inv(Sw) * cov(means); 
[eigvec2, eigval2] = eig(criterion);

See {587} for results on EMG data.

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ref: -0 tags: LDA PCA autoregressive EMG date: 07-29-2008 23:35 gmt revision:3 [2] [1] [0] [head]

Below, emg classification by computing the autoregressive coefficients and feeding them into linear discriminant analysis (LDA). LDA code from here; data in myopen svn. Nine classes of movement in the data, 4 repetitions of each. The input data is 16-dimensional: 4 AR coefficients per 4 channels. This is consistent with Blair Lock's thesis.

For reference, here is an imagesc() of the raw coefficients (the 4 different color bands correspond to the 4 different channels):

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ref: -0 tags: myopen EMG recordings NLMS noise date: 07-29-2008 18:32 gmt revision:2 [1] [0] [head]

Myopen amplifiers & analog/digital filters & NLMS are working properly! Below, a recording from my deltiod as I held my arm up: (only one EMG channel active, ground was my knee))

Yellow traces are raw inputs from ADC, blue are the output from the IIR / adaptive filters; hence, you only see 8 of the 16 channels. Read from bottom to top (need a -1 in some opengl matrix somewhere...) Below, the system with no input except for free wires attached to one channel (and picking up ambient noise). For this channel, NLMS could not remove the square wave - too many harmonics - but for all other channels the algorthim properly removes 60hz interference :)

Now, let me clean this EEG paste off my shoulder & leg ;)

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ref: notes-0 tags: software debian xpaint maxima math mathematica date: 07-02-2008 14:37 gmt revision:3 [2] [1] [0] [head]

oldies but goodies:

  • Maxima a computer algebra system, almost like a free version of Mathematica!
    • be sure to install maxima-emacs to get LaTeX prettyprinting.
  • [xpaint] Has a cool spring-mass-friction system where the length of the spring (the distance between cursor and paint brush) controls the width of the paint brush. see below!

Both are in Debian of course :)

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ref: notes-0 tags: expectation maximization EM clustering autosorting date: 06-16-2008 19:40 gmt revision:5 [4] [3] [2] [1] [0] [head]

so, I coded up the EM algorithm - it was not hard, though i did have to put the likelihood calculation in C++ because i couldn't figure out how to vectorize it properly. It fits the clusters pretty well, but it does not tell you how many clusters there are!

clustering with 5 underlying gaussians:

plot of the log-likelihood of fitted gaussian mixtures vs. number of gaussians:

the code is in subversion, of course.

James has code for gibbs-sampling to the correct number of components! Here is an example of the output - it quickly removes the unnecessary gaussian components:

images/227_4.pdf -- original CEM (classification expectation maximization) paper, 1992, by Celeux and Govaert. Note that CEM with no variance estimation and gaussian clusters is the same as k-means, see {224}. See also http://klustakwik.sourceforge.net/

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ref: bookmark-0 tags: memory supermemo leraning psychology Hermann Ebbinghaus date: 05-08-2008 15:25 gmt revision:0 [head]

http://www.wired.com/medtech/health/magazine/16-05/ff_wozniak -- wonderful article, well written. Leaves you with a sense of Piotr Wozniak (SuperMemo's inventor) crazy, slightly surreal, impassioned, purposeful, but self-regressive (and hence fundamentally stationary) life.

  • Quote: SuperMemo was like a genie that granted Wozniak a wish: unprecedented power to remember. But the value of what he remembered depended crucially on what he studied, and what he studied depended on his goals, and the selection of his goals rested upon the efficient acquisition of knowledge, in a regressive function that propelled him relentlessly along the path he had chosen.
  • http://www.wired.com/images/article/magazine/1605/ff_wozniak_graph_f.jpg
  • Quote: This should lead to radically improved intelligence and creativity. The only cost: turning your back on every convention of social life.

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ref: notes-0 tags: flock OSS opensource Mozilla LGPL money corporations browser comment embedded mobile opera date: 12-20-2007 16:23 gmt revision:0 [head]

I'm posting my comments about http://flock.com/ here just in case they are removed from the actual site

This is all very interesting. I just downloaded it, and flock seems to work well. I'm probably not going to use it unless there is some demonstrable technical superiority (e.g. leaks less memory than firefox), as the social sites just distract me from getting work done.

Anyway, I have a question: how are you going to make money? How are you paying the developers? If you are not and it is all OSS, where is the source? It seems like the VC's are just throwing money away for the (hypothetical) good of the social-network crowd. Or, rather, you are indirectly funding the popularity of sites that flock makes it easy to get at. Are these sites (e.g. facebook) paying you? Wait -- flock allows you to look at content and not the ads. They are not paying you.

Perhaps you are moving along the lines of Opera, and intending to get people addicted to flock to a degree that they demand it on their mobile devices. Mobile devices are closed (for now .. check google), hence you can make money licensing software to phone manufacturers. I imagine that you'll have to rewrite the Mozilla core to do this (unless phones become significantly more powerful - not likely, they are battery devices. ) Mozilla is (L)GPL - you'll have to release the source. To the best of my knowledge, with non-physical goods money can only be made from gradients in knowledge (pun.. intended), therefore you will have to keep the source closed. If this is the case, you'll be able to make money (on this, i don't know what else you have planned) for a while, and when you can no longer, I hope you open the source like netscape.

Technically, though, excellent job! your website is also very pretty!

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ref: engineering notes-0 tags: bluetooth CSR NXP headset radio telemetry 802.11 zigbee low-power date: 12-12-2007 06:10 gmt revision:26 [25] [24] [23] [22] [21] [20] [head]

the contenders:

  • NXP BGB210S, a 4th generation chip from Philip's spin-off NXP.
    • 3 x 5 x 1 mm (!!).
    • supports Bluetooth 2.0 + EDR.
      • the higher data rate is really targeted at decreasing the TX active time.
    • power consumption: 12ma @ 1.8v supply = 21.5mW
    • CMOS w/ near-zero intermediate frequency radio.
  • BGW211 802.11 system-on-a-chip, from NXP
    • 400mw Tx power, 300mw Rx - both too much, me thinks.
  • BCM4326
    • similar power figures (295mw rx, 425mw tx)
    • ultra-small 0.25mm WLCSP (!!)
    • one chip solution for 802.11b/g ; BCM4328 supports 802.11a, too.
  • Wi2wi w2cbw003 - 230ma tx, 210ma rx 802.11 module. too much.
  • G2 Microsystems , developers of 802.11B (11mbps) system-on-a-chip for RFID.
    • insanely low power dissipation! years on AA batteries! (based on 40s interval between data transmissions. 1.3mJ per transmission - an order of 500 lower than existing solutions. much lower static dissipation, too.
    • includes 32 bit RISC processor with 80kb ram, 320 kb flash/rom.
    • works with existing infrastructure, e.g. cisco.
    • article on the chip / product, may 2006.
    • http://www.gainspan.com/ -- competitors. they do not appear to have a product yet.
  • Freescale LP1070FC 802.11a/b/g, requires external PA, LNA, switch. no data on the power consumption... actually, the datasheet appears to be rather incomplete!
  • CSR UniFi-1 radio is far better, but I can't seem to find documentation for that, specifically the power dissipation.
    • well, let's see - 20 hours talk time with a 1500mAH battery = 75ma. not bad, i guess; TX power can be decreased for the short range we need.
  • BlueCore5 ; product brief, which includes more power info.
    • rather recently developed; is the silicon debugged? The datasheet is preliminary information.
    • 10 x 10mm, 0.8mm pitch 105 balls or 8x8 TFBGA.
    • 1.5V core, 1.8V-3.6V io, USB
    • bluetooth v2.0/2.1.
    • 64mips DSP on-chip.
      • 0.3 mA/MIPS at 1.5V. compare to Texas instruments TMS320VC5507 = 0.45ma/Mhz @ 1.2V core ~= 54Mw at 100mips; Kalimba ~= 30mw @64mips.
      • this is 2mips/channel. enough? damn, gotta keep the power low!
      • the Bluecore3 datasheet has more information on the DSP power consumption.
    • 16 Mbit flash, too!
    • BlueCore4 seems to be much better documented, but it does not include the DSP, which saves a lot on parts count.. as well as power.
  • Bluecore4
    • 8x8mm 96-bga,
    • with 6mbit flash!
    • bluetooth 2.0 / EDR.
    • current consumption of about 26ma @ 1.8V supply when in SCO HV3
  • Boroadcom BCM4326
    • single chip 802.11b/g solution, integrated Arm7 CPU
    • again, 300mW (not mA! smaller!) Rx, 400 mW Tx. That's still a lot of power.
  • Broadcom BCM2047
    • again, seems that these have yet to come out; details are scarce.
    • The belkin bluetooth 2.0 adapter that I bought at compusa uses a BCM2045.
  • CC2400, non-bluetooth - simpler!
  • Zarlink - ultra low power, 433Mhz ISM band biomedical tranciever.
    • about 7x7mm.
    • 3v supply, 2.7 should work, 5ma = 13.5mw (yesss!)
    • 800kbps raw data rate, max.
    • 2.45Ghz wakeup reciever (??)
    • seems to be designed for pacemakers & neurostimulators.
    • need to contact zarlink for the full data sheet.
  • RFM TR1100 - what the wolf lab uses for telemetry. OOK or ASK.
    • 1Mbps max.
    • only 1 channel, so far as i can tell...
    • 8ma @ 2.7V = 21.6mw.
    • integrated SAW filters = narrow bandwidth.
    • 10 x 6 mm size, minimal external components, though it does seem to require extra resistors.
    • really interesting method of obtaining RX input amplification stability - a SAW delay line, where the amplifiers are pulsed on at different times to permit the passage of RF energy. quote: "rf stability is obtained by distributing RF gain over time", as opposed to the superheterodyne solution of distributing gain over frequencies. If there were 100db of gain in 1 frequency, the amplifier is very likely to oscillate.
  • RFM TRC101
  • RFM TR3100 576 kbps ASK, -85dbm 10e-3 error rate.
    • 10ma TX / 7ma RX
    • 11x9,65mm SM-20L package
    • kinda has a lot of external components.
    • 434 mhz operation.
  • ADF7025 Analog 431-464, 862-870, 902-928 ISM band FSK transceiver.
    • 20ma TX (28ma at +10dbm) , 20ma RX from 2.3 to 3.6V supply.
    • direct conversion: zero IF.
    • SPI interface (plus a bunch of other signals).
    • 384kbps max data rate.
    • 7mm x 7mm 48 lead CSP
  • CC1101 (chipcon was acquired by TI)
    • 500kbps FSK, GFSK, MSK, OOK, ASK transmit/receive. 500kbps is only available in MSK, minimum-shift keying mode.
      • -84dbm receiver sensitivity @ 500kbps.
    • same bands as above + a bit more margin.
    • suitable for frequency hopping systems.
    • QLP 4mm x 4mm package
    • 16ma TX (32.3 at +10dbm), 16ma RX
    • 1.8 - 3.6V operation.
  • Freescale MC13192, Zigbee compliant transciever, 2.405 - 2.480 Mhz, 5mhz channels, 2Mchip/sec over the air data rate, 200kbps practical rate.
    • 30ma TX @ 0dbm, 37ma RX.
    • 5mm x 5mm package
    • full Zigbee PHY support.
    • similar device from ember - 35ma TX / 35ma RX, 2.1-3.5V, 7x7mm, includes microprocessor.
    • MC13201 - also targeted at 802.15.4 compliance.
      • good to 250kbps, 5.0 mhz channels, DSSS, 2.0 - 3.4V,
      • requires external transmit/receive switch
      • 30ma TX / 37ma RX
      • 5 x 5mm package
  • TI / Chipcon CC2430 - 27ma TX / 27ma RX, with microcontroller, 7x7mm package, quick power-up.
  • Cypress wireless USB
    • 2.4ghz, 1mbps, DSSS encoding (like zigbee) -- DSSS reduces the data rate, of course; the data rate over the air is always 1mbps.
      • the favored rate is 8x DSSS, where each symbol encodes one byte (8 bits) but requires 32 or 64 chips for transmission (resulting in a net rate of 250kbsp or 125 kbps, like zigbee. )
    • 21ma normal operating current @-5dbm, 1.8V to 3.6V
    • 6mm x 6mm 40-lead package
    • document above is a generally good overview of the complexities of this type of design.
  • SC1211
    • 110kbps, UHF transceiver (~863 - 960mhz). very low power consumption in RX: 3ma / TX: 25ma @ +10dbm out
    • out November 2007.
    • competitor to below -- much lower RX power (and lower rate).
    • includes 64byte fifo, data whitening, etc.
  • advance info from Maxim
    • very low power TX: 4ma @ -10dbm, RX: 150ua Hey.. that's lower power than most PLL, VCO, & PA put together!
    • OOK, ~116kbps.
  • Nordic nRF24L01 - THE BEST (so far!)
    • datasheet.
    • 12ma TX/RX, 2.1 3.6V
    • 2mbps over-the-air rate, GFSK, 10m range (better with a bigger antenna)
    • 4x4mm 20 pin package.
    • 125 selectable channels.
    • allows clock sharing with a microprocessor, e.g. the blackfin, provided it exceeds the 60ppm specification.
    • 22ua power consumption in standby-1 mode (transition from this state to RX/TX in 130us), 320ua power consumption in standby-2 mode (ready to transition to TX)
      • it is important to never keep the nRF24L01 in TX mode for more than 4ms at a time (!)
    • i think the designer's confidence showes through the specification sheet: they are proud of the chip & it's specifications, which is a very good thing. it means they put some pride and passion into it.
    • development board - need to buy! They also [distribute the IC http://www.sparkfun.com/commerce/product_info.php?products_id=690], yay!

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ref: notes-0 tags: gcc inline assembler blackfin date: 11-22-2007 19:13 gmt revision:3 [2] [1] [0] [head]

So, you want to write inline assembly for the blackfin processor, perhaps to speed things up in a (very) time-constrained environment? Check this first:

  • calling ASM from C on a Blackfin
  • Inline assembly with gcc (general)
  • gcc manual entry for constraints (general)
  • The general format is, as per the refs, asm("some assembly":"output constraints"(c out args):"input constraints"(c in args):"clobbered regs");
  • 'volatile' just means that the compiler should not move the instruction around and/or delete it. This may actually be good for checking - if you tell gcc that it may not delete an instruction, but gcc doesn't know where to put it, it will complain -- and not compile.
  • If you are using C / C++ preprocessor macros in the inline assembly, you must first compile the C code down to assembly (using -S flag), then run gcc with the flag -x assembler-with-cpp As the C preprocessor macros are necessarily in headers, just include them on the command line (e.g. in the makefile) with the -include flag.

Nobody seems to have a complete modifier list for the blackfin, which is needed to actually write something that won't be optimized out :) here is my list --

  • d -- use a data register, e.g r0 - r7. don't use 'r' for this ala x86 !
  • a -- use one of the addressing registers.
  • = -- register is written (output only)
  • + -- register is both read and written (output only)


  • asm volatile("%0 = w[p5];":"=d"(flags));
    • flags should be in a data register, it is written output.
  • asm volatile("bitclr(%0, RS_WAITIRQ_BIT)":"+d"(state));
    • state must be in a data register and it is both read and written (which is true - a bit is modified, and the input state matters). Must be an output register, not an input -- you cannot use the '+' constraint with inputs.

Constraints for particular machines - does not include blackfin.

  • however, it should be in the gcc tree -- and, well, the source is online...
  • here are the comments from /gcc/config/bfin/bfin.md :
; register operands
;     d  (r0..r7)
;     a  (p0..p5,fp,sp)
;     e  (a0, a1)
;     b  (i0..i3)
;     f  (m0..m3)
;     B
;     c (i0..i3,m0..m3) CIRCREGS
;     C (CC)            CCREGS

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ref: -0 tags: blackfin BF532 memory map date: 11-21-2007 21:18 gmt revision:1 [0] [head]

page 6 on the spec sheet. 55

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ref: bookmark-0 tags: EMG apples Japan food date: 11-12-2007 17:51 gmt revision:1 [0] [head]

Electromyography of Eating Apples: Influences of Cooking, Cutting, and Peeling

  • good lord, this is retarded research!

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ref: De-2007.02 tags: perkinsons REM RBM behavior disorder date: 11-11-2007 05:43 gmt revision:1 [0] [head]

PMID-17235126[0] Restoration of normal motor control in Parkinson's disease during REM sleep.

  • wow! but, hasn't this been known for a while?


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ref: Clancy-2007.09 tags: EMG channel equalization filter date: 11-11-2007 05:04 gmt revision:0 [head]

PMID-17614134[0] Equalization filters for multiple-channel electromyogram arrays.

  • idea: use digital filtering to equalize (as in communication systems) each electrode in a large array, and then use this to drive the common-mode (digital) rejection.


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ref: bookmark-0 tags: emate wireless date: 11-09-2007 17:02 gmt revision:0 [head]

http://geektechnique.org/projectlab/669/getting-your-emate-wireless -- self-explanatory. now, all i need to do is get a waveLAN card (as well as reinstall classic on my g4)

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ref: notes-0 tags: Nordic RD nrf24l01 problem transceiver date: 11-02-2007 18:00 gmt revision:4 [3] [2] [1] [0] [head]

here is the final, concluding, email i sent to nordic semiconductor concerning my 'troubles' with their chip. I post it here in hopes that it may help somebody else out there via the magic of the internet. See {485} for the development of the mode-switch solution (2) and {484} for the dropped packet investigation.

Hi xxx,

Ok, i figured out both of my problems:

  1. The missing RX_DR IRQ was because I was clearing the RX fifo upon reading outone packet. Because a packet was being received while the SPI was reading it out (the PTX is continually transmitting), this caused the radio to drop the packet before it was completely received. Dumb! dumb!
  2. Concerning my old problem of lost packets during mode switches, I needed to do a number of things to get it to work:
    1. Add inline resistors to keep spi noise out of the radio
    2. Increase the SPI clock on both PTX and PRX, to avoid not being able to read out the packet after one IRQ and before another was received (as you suggested below).
    3. Added a 62us (of course, longer delays also work) delay between transitioning from RX mode to TX mode. During this time I do not assert CE. A delay in for the opposite transition is not needed. Not exactly sure why this is needed, but it works!
    4. On the PRX, when i send the 'acknowlegement' packet, it is necessary to only pulse CE after uploading the packet. Holding CE high until TX_DS IRQ is asserted somehow messes things up. I guess this is described on the state diagram on your spec sheet - it is best to go back into standby-I mode not standby-II, as there is no transition to RX mode from standby-II.

As a result, I'm getting 99.99 % reliability on bidirectional bandwidth of 1.39mbps PTX->PRX and 18.3kbps PRX->PTX. So, I'm a happy person :) :) Hence, I don't have to try another radio solution.

Just wanted to pass the information along in case it would help your other customers.

cheers, Tim Hanson

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ref: notes-0 tags: Nordic RF problem fifo transceiver date: 11-02-2007 17:59 gmt revision:14 [13] [12] [11] [10] [9] [8] [head]

Problem: switching modes on the nordic radios. see also {486}

  1. Standard scheme:
    1. headstage sends 16 data packets as per {484} by keeping the fifo continuously full, then send a status packet (as indicated by the first two bytes), then transitions to RX mode and waits for a reply.
    2. bridge listens & reads in each packet, again as {484}; when it notices that a status packet has been received, it immediately transitions to TX mode and sends out a packet. After waiting for the TX data sent IRQ, it then transitions back into RX mode.
      1. note: at present transitioning between modes flushes both RX and TX fifos.
    3. result: bridge gets 52% of all status packets sent by the headstage ;
    4. result: headstage, in return, receives replies to 47% of its status packets (that means the bridge transmission efficiency is 90.6%. This does not depend on the headstage matching the 2-byte code.
  2. If the headstage only sends one data packet prior the status packet, transmission reliability is not affected
  3. If the headstage sends no data packets hence only the status packet prior to waiting for a reply, the bridge hears exacly half of the status packets, while the headstage seems to hear every one of the bridge's replies.
    1. adding a small delay after setting TX mode marginally affects the reception ratios 51.2% headstage status packets are seen by the bridge, and ~ 90% of the bridge's replies are seen by the headstage.
  4. ok! new development: if a 62us (exactly) delay is inserted between reading out the status-ack packet on the headstage & transitioning to TX mode (e.g. BEFORE transitioning to TX mode), almost all the status packets are received (99.6%). however, if the delay is inserted AFTER transitioning to TX mode, half (exactly) of the status packets are lost. In both cases, the software on the bridge is the same. ''The delay should be essential, as it takes a bit longer for the bridge to switch back to RX mode after transmitting the packet than the headstage from RX to TX. Both transitions are synchronized by TX_DS IRQ on the bridge and RX_DR IRQ on the headstage.
    1. if, upon switching modes, we don't flush both fifos, reliability is decreased (with the 62us delay before switching modes) to 92% status packets received, 83% overall replies sseen on headstage.
    2. the same delay between RX and TX mode is not the same on the bridge -- adding it before decreases performance, adding it after transitioning & before DMA improves performance, provided the TX fifo is flushed upon transition. 100us too much.


  1. put a 62us (minimum) delay between reading in the status ACK packet on the headstage & transitioning to TX mode, to allow the bridge to be in RX mode before we send anything. Presently, i deassert CE during the delay.
  2. only pulse CE on the bridge when sending the packet, don't hold it high until the TX_DS IRQ is asserted. This leaves it in Standby-I mode, not standby-II.
  3. flush the appropriate TX or RX fifo whenever transitioning between modes (e.g. flush the TX fifo after going to TX mode). Not sure why - perhaps it is needed when starting or recovering from lost packets - but it works! Fifos are flushed after writing the configuration register.

present performance:

  txed packets = 118513 
  rxed packets = 118218 
 (note: computer has seen 118512 packets )
 (and 118414 status packets, ratio: 0.999165 )
 (note: 'stage ratio 0.997511 )
(this includes code validation)

now, if i boost the SPI clock on the bridge up to 5 mhz (headstage clock still running at 8.25mhz) to eliminate race-case (?) & add in 16 data packets before the status packets, perfection:

 txed packets = 44151 
 rxed packets = 44151 
 (note: computer has seen 750583 packets )
 (and 44152 status packets, ratio: 1.000023 )
 (note: 'stage ratio 1.000000 )
after adding separate counters for TXed status and TXed data packets:
  txed packets = 808640 
  rxed packets = 50538 
  txed status packets = 50540 
 (note: computer has seen 808639 packets, ratio : 0.999999 )
 (and 50540 status packets, ratio: 1.000000 )
 (note: 'stage ratio 0.999960 )
yay!! almost no dropped packets!!

This equates to :

  • a net incoming (to headstage) bandwidth of 32 * 8 / 3.48ms = 73.5 kbps. This seems like more than plenty - how much do we have to say to the bugger? 1/4 this bandwidth is probably sufficient = 18.375 kbps, or one 32 byte packet every ~ 11.2ms.
  • a net outgoing (from headstage) bandwidth of 32*16*8 bits / 3.48ms = 1.17 mbps
    • this can be tuned further by making the status packet transmission less frequent than once every 16 data packets.
    • 16 packets, with 3 byte address & 2 byte CRC, takes 2.72ms to transmit (CE enable to CE disable); hence, we should be able to transmit 23 packets within the 4ms time that the PLL is synched. ah, but 16 is such a nice number... and it is better not to get too close to device limits.
    • The maximum, excluding PLL resynchs, 1.6mbps (takes 160us to transmit a packet with 2 byte CRC, 5 byte address, and 32 data bytes).
    • The maximum, including pll resynchs every 4ms or less, is about 1.5mbps;
  • With status packet every 4th 16-packet block, we got an observed rate of 1.39mbps (empirical, based on computer's clock, including header, 5431.5 packets/sec) and an incoming bandwidth of about 18.3kbps (as above). This is enough to support 170 spikes/sec (28 sample waveform + header) on each of 32 channels!! :)
  • This is summarized in {487}

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ref: notes-0 tags: Nordic problem fifo transceiver email date: 11-02-2007 17:58 gmt revision:3 [2] [1] [0] [head]

here is an email I wrote to nordic semiconductor technical support concerning switching reception/transmission modes. see also {487} & {485}


I've been having problems with switching modes on the nRF24L01. I want to implement a asymmetric bidirectional link, where there is a periodic (every ~36ms) time when the primary transmitter sends a status packet, then listens for a 32-byte command packet from the primary receiver. The command packet is for conveying configuration information, etc. I am driving both radios with blackfin DSPs using the built-in SPI port @ 4mhz, and am very careful with the CSN signal. The shock-burst feature is not enabled.

Unidirectional transfer works great - I get nearly 0% dropped packets when the primary transmitter & receiver never change modes, up to a rate of about 1.5mbps. Of course, I am careful not to let the radio stay in TX mode for more than 4 ms - every 3ms i give it a 'break' by de-asserting CE.

But bidirectional does not work reliably. Here is my procedure, on the primary transmitter side, for sending a status packet then changing from TX to RX & back to TX, with the initial condition that CE is asserted:

  1. wait until the TX fifo is empty by polling the FIFO_STATUS register through spi
  2. clear TX_DS interrupt in status register
  3. send packet with code 0xa0
  4. wait for TX_DS interrupt on IRQ
  5. deassert CE
  6. flush the RX fifo code 0xe2 (not sure if this is needed, but somehow, it improves reliability).
  7. write the config register with the following bits set: MASK_TX_DS | MASK_MAX_RT | EN_CRC | CRC0 | PWR_UP | PRIM_RX
  8. clear interrupts by writing 0x70 to register 0x07
  9. assert CE
  10. wait for RX_DR iterrupt on IRQ (e.g. wait for a packet from the primary receiver - the reciever has to both read in the status packet and send out command packet through SPI, hence must wait for 544us )
  11. clear interrupts again
  12. read in packet w / 0x61 command
  13. deassert CE
  14. write the config register with the following bits set: MASK_TX_DS | MASK_MAX_RT | EN_CRC | CRC0 | PWR_UP
  15. clear interrupts by writing 0x70 to register 0x07
  16. assert CE

The process on the primary receiver is basically the same, but inverted. Upon receiving a packet of the correct type, it switches to transmit mode, sends off a packet, waits for the TX_DS interrupt, and switches back to RX mode.

Like I said, when the transmitter and receiver never switch modes, the packets always get through without any corruption. When they switch roles for one packet, only ~ 78% get through, making the status packet -> command packet reply about 62% reliable. This is when the radio is only sending status packets - hence mostly it is in what the datasheet calls 'standby-II mode'. When the radio is also transmitting data packets, the status packet -> command packet relay is about 79% reliable, suggesting that the first packet after a switch from RX to TX mode is somehow being lost. Indeed, when I look at the IRQ signals on an oscilloscope, it is apparent that a certain percentage of the time the TX_DS interrupt is not followed by a RX_DR interrupt.

so - what am I doing wrong??!! I'm desperate to make this work, and have tried almost every permutation!

thanks, Tim Hanson

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ref: notes-0 tags: nordic RF problem fifo date: 11-01-2007 18:03 gmt revision:16 [15] [14] [13] [12] [11] [10] [head]

experimential results with the Nordic nRF24l01 (recall, as per {477}, that all SPI signals have an in-line 100 ohm resistor on both the headstage and bridge)

  1. If the bridge is a transmitter & the headstage is a receiver, we let the DMA finish before asserting CE, we continuously pool the SPI bus on the headstage, and read the packet entirely in, then almost all packets get through (by my rough oscilloscope measuring). This requires 420us per packet, 290 to actually transmit the packet and 130 for SPI maneuvers.
    1. If this direction is reversed (normal: headstage is the transmitter & bridge is the receiver), the packet reliability is increased. The headstage takes 350us to transmit one packet -- 60us for SPI transation ( can run at a higher speed, as it the bus traces on that board are smaller). However, the reliability of the link in both regimes > 95%.
  2. If the fifo is filled (3 packets) on the headstage side, then CE is pulsed, only the first and third packet get through, with 320 us between each packet. again, SPI bus is polled on the bridge (receiver) side. Reception of the first packet is reliable, reception of the third is not so much. Clearing the RX fifo increases reliability of receiving the third packet.
    1. This is not affected by disabling 2-byte CRC.
    2. This is not affected by doing non-dma SPI transfers.
    3. If i press my finger on the crystal oscillator next to the nRF24l01 (presumably thereby affecting tuning, sometimes only the second packet is received , but still never all 3.
  3. Now, if we offer the same regime & listen for the IRQ pin to go low, then clear the status, the bridge receives all 3 packets, but not always - it only gets every other group of 3 packets.
    1. This is not affected by disabling CRC, nor does it seem to be affected by channel selection.
    2. This is dependent on clearing the RX fifo after reception -- of course, clearing the RX fifo while a packet is in the air will cause that packet to be rejected
  4. If the fifo is not filled (2 packets) on the headstage (= transmitter), then CE is pulsed, the first packet gets through, and the second one does, too (sometimes)
    1. This is not affected by disabling CRC nor by changing the radio channel.
  5. If the fifo is not filled (2 packets) on the bridge (= transmitter), then CE is pulsed, only the first packet gets through
  6. if we reverse the order - have the bridge send 3 packets over SPI then assert CE, and set the headstage to listen & immediately clear the IRQ on interception, the first packet is most always received, the third packet is usually received, but the second packet is never received.
    1. if we do the same as above, and read the entire packet over SPI & use polling to see when another packet is received, then once again both the first and second packets are received properly. The polling implies that noise from the SPI bus is not corrupting packet reception.
  7. Again, if we do pipelined transmission from headstage to bridge by keeping the fifo consistently full, then every other packet is dropped -- see {477}
I wish i had pictures for this.. wish i had a webcam!

finally, it is solved!

  1. don't clear the IRQ status multiple times
  2. don't poll the bus to see if there is a packet - wait for the IRQ pin, then read the packet in.
  3. don't clear the TX fifo or RX fifo, except in time of error or when starting up.
  4. keepin the fifo one-full works with a headstage (= transmitter) SPI clock of 8.25mhz. at this rate, can upload 2 packets before 1 is transmitted.

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ref: notes-0 tags: nordic fifo problem radio tranciever date: 11-01-2007 05:50 gmt revision:5 [4] [3] [2] [1] [0] [head]

I've been having problems transmitting packets in a pipelined fashion using the nordic nRF24L01 tranciever IC. Namely, I cannot send multiple packets at once by keeping the on-chip 3-packet fifo full (note packets are 32 bytes data, max; with header/CRC, they are close to 40 bytes). If this fifo is full, the radio should remain in transmit mode - see {470}, and also {484}

Above, what happens when I let the fifo go dry / empty, and force the PLL to resync for each transmitted packet, as per the following sequence:

  1. write a packet via SPI
  2. assert CE
  3. wait for the TX data sent IRQ to be asserted (0). This requires pll resync (130 us) and packet transmission (160us @ 2mbps)
  4. clear IRQ via SPI
  5. deassert CE
  6. if packet # < 16, go back to step 1.
The bottom trace is the receiver's IRQ line, which is 0 when data is received. I send them in groups of 16 so as to be as idential as possible to below, where it is necessary to get the radio out of TX mode to re-synch the pll. (however, it is possible to send indefinitely in this way..) On the receiver side, packets are either read in using DMA, or the RX fifo is cleared using the associated SPI command - either one works fine.

Note just about all packets are received properly and that the RX irq closely follows the TX irq.

Above, what happens when i try to pipeline transmission, e.g.

  1. upload a packet via SPI
  2. assert CE, force PLL to re-sync
  3. upload another packet
  4. wait for the data sent IRQ from the preceding packet & clear IRQ using SPI
  5. if packet number < 16, go back to step 3
  6. otherwise, wait for the final packet IRQ & clear using SPI
  7. de-assert CE
  8. wait 1ms or so.
The sequence should force the radio to stay in transmit mode for 16 sequential packets, and indeed the transmitter's IRQ line reflects this. However, only half of the (seemingly) transmitted packets are received, as indicated by the receiver's IRQ line, the bottom trace. What? I've been careful not to exceed the 4ms pll resync timeout. Also, the order of received packets sometimes changes - usually it gets the first packet in a group, but sometimes it does not (left segment of bottom trace).

One initial theory was that noise on the SPI bus was corrupting the packets. However:

  • If it was noise on the transmitter's bus, then every packet would be corrupted, as there is transmitter SPI activity (e.g. packet upload) during radio transmission of all but the last packet in a group.
  • If it was noise on the receiver, then continually polling the 'status' (0x07) and 'fifostatus' (0x17) registers would cause more packets to be dropped. However, i can poll using SPI with the transmission scheme in the first figure (wait for packet to be on the air before sending another), and will not get any more dropped packets than usual.
  • I tried adding 100 ohm resistors to all SPI signals on both the transmitter and receiver, but this did not affect the problem.
This is somewhat of a show-stopper for me...
  • If it takes 330us (130us pll sync, 160 to transnmit, 40us for SPI delays) to send 32 bytes, then the aggregate rate is only 775kbps, or 38% utilization of the 2mpbs radio, equivalent to a max 108 wf/sec/32 channels.
  • If i can use the fifos and pipeline the transmission, can get > 1.08 mbps, or 54% utilization, or 150wf/sec/32channels.
The latter is much more acceptable, as neurons can have firing rates > 100hz but not usually > 150hz.

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ref: notes-0 tags: Blackfin perl loopcounters registers ABI application-binary interface gcc assembly date: 10-19-2007 17:24 gmt revision:2 [1] [0] [head]

The problem: I have an interrupt status routine (ISR) which can interrupt the main, radio-servicing routine at any time. To keep the ISR from corrupting the register values of the main routine while it works, these registers must be pushed, and later popped, to the stack. Now, doing this takes time, so I'd prefer to pop / push as few registers as possible. Namely, I don't want to push/pop the hardware loop registers - LC0 (loop counter 0), LB0 (loop bottom 0, where the hardware loop starts) & LT0 (loop top 0, where the hardware loop ends).

Gcc seems to only touch bank 1, never bank 0, so I don't have to save the 3 regs above. However, to make sure, I've written a perl file to examine the assembled code:

my $file = "decompile.asm"; 
open(FH, $file); 
@j = <FH>; 
my $i=0; 
my @badregs = ("LC0", "LB0", "LT0"); 
foreach $reg (@badregs){
	foreach $k (@j){
		if($k =~ /$reg/){
			print "touch register $reg : $k";
#tell make if we found problems or not.
	exit 1;
	exit 0;

'make' looks at the return value perl outputs, as instructed via the makefile (relevant portion below):

	rm -f *.ldr
	$(LDR) -T BF532 -c headstage.ldr $<
	bfin-elf-objdump -d headstage.dxe > decompile.asm
	perl register_check.pl

if it finds assembly which accesses the 'bad' registers, make fails.

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ref: bookmark-0 tags: embedded linux eCos ARM intellectualProperty IP MIPS Xscale MIT date: 10-08-2007 17:55 gmt revision:0 [head]

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ref: notes-0 tags: old blackfin code assembly date: 09-11-2007 15:52 gmt revision:2 [1] [0] [head]

abandoned because I realized that I could work on 2 channels at once (as there are 2 MACs onboard) & could use the s2rnd multiply-accumulate flay & could load registers 32bits at a time! ah well, might as well archive my efforts :)

	r6.h = 2048; 
	r0.l = r0.l - r6.h (s) || r1.l = w[i0++] || r2.l = w[i1++]; //subtract offset, load a1[0] into r1.l, w1[0] into r2.l
	a0 = r0.l * r1.l (is) || r1.h = w[i0++];  //mac in*a1[0], load a[1] to r1.h
	a0 += r2.l * r1.h (is) || r1.l = w[i0++]|| r2.h = w[i1--]; //mac w[0]*a1[1], load a1[2] into r1.l, w1[1] to r2.h
	r4 = (a0 += r2.h * r1.l) (is) || r3.l = w[i0++]; //mac w1[1]*a1[2] store to r4, b1[0] to r3.l
	r4 = r4 >>> 14 || r3.h = w[i0++]; //arithmetic right shift, 32 bit inst, b1[1] to r3.h, r4 is new w1. 
	a0 = r4.l * r3.l (is) || w[i1++] = r4.l; //mac w1*b1[0], save w1 into w1[0]
	a0 += r2.l * r3.h (is) || w[i1++] = r2.l; //mac w1[0]*b[1], save w1[0] into w1[1]
	r4 = (a0 += r2.h * r3.l) (is) || r1.l = w[i0++] || r2.l = w[i1++];//mac w1[1]*b1[0] store r4, a2[0] to r1.l, w2[0] to r2.l
	r4 = r4 >>> 14 || r1.h = w[i0++] || r2.h = w[i1--]; //arith. right shift, a2[1] to r1.h, w2[1] to r2.h 
	a0 = r4.l * r1.l (is);  //mac in*a2[0],  a2[2] into r1.l
	a0 += r2.l * r1.h (is) ||  rl.l = w[i0++]; //mac w2[0]*a2[1], b2[0] into r3.l
	r4 = (a0 += r2.h * r1.l) (is) || r3.l = w[i0++]; //mac w2[1]*a2[2] store r4, b2[1] into r3.h
	r4 = r4 >>> 14 || r3.h = w[i0++]; //arithmetic shift to get w2, b2[2] to r3.h
	a0 = r4.l * r3.l (is) || w[i1++] = r4.l; //mac w2 * b2[0], store w2 to w2[0]
	a0 += r2.l * r3.h (is) || w[i1++] = r2.l; //mac w2[0]*b2[1], store w2[0] to w2[1]. i1 now pointing to secondary channel. 
	r4 = (a0 += r2.h * r3.l) (is) || i0 -= 10; //mac w2[1]*b2[0].  reset coeff ptr. done with pri chan, save in r5.
	r5 = r4 >>> 14; 
	//time for the secondary channel!
	r0.h = r0.h - r6.h (s) || r1.l = w[i0++] || r2.l = w[i1++]; //subtract offset, load a1[0] to r1.1, w1[0] to r2.l
	a0 = r0.h * r1.l (is) || r1.h = w[i0++] ; //mac in*a1[0], a1[1] to r1.h, save out samp pri.
	a0 += r2.l * r1.h (is) || r1.l = w[r0++] || r2.h = w[i1--]; //mac w1[0]*a1[1], a1[2] to r1.l, w1[1] to r2.h
	r4 = (a0 += r2.h * r1.l) (is) || r3.l = w[i0++]; //mac, b1[0] to r3.l
	r4 = r4 >>> 14 || r3.h = w[i0++]; //arithmetic shift, b1[1] to r3.h
	a0 = r4.l * r3.l (is) || w[i1++] = r4.l; //mac w1*b1[0], save w1 to w1[0]
	a0 += r2.l * r3.h (is) || w[i++] = r2.l; //mac w1[0], save w1[0] to w1[1]
	r4 = (a0 += r2.h * r3.l) (is) || r1.l = w[i0++] || r2.l = w[i1++]; //mac w1[1]*b1[0] store r4, a2[0] to r1.l, w2[0] to r2.l
	r4 = r4 >>> 14 || r2.h = w[i1--]; // r4 output of 1st biquad, w2[1] to r2.h
	a0 = r4.l * r1.l (is) || r1.h = w[i0++] ; //mac in* a2[0], a2[1] to r1.h
	a0 += r2.l * r1.h (is) || r1.h = w[i0++] ;  //mac w2[0]*a2[1], a2[2] to r1.l
	r4 = (a0 += r2.h * r1.l) (is) || r3.l = w[i0++]; //mac w2[1]*a2[2], b2[0] to r3.l
	r4 = r4 >>> 14 || r3.h = w[i0++]; //r4 is w2, b2[2] to r3.h
	a0 = r4.l * r3.l (is) || w[i++] = r4.l ; //mac w2 * b2[0], store w2 to w2[0]
	a0 += r2.l * r3.h (is) || w[i++] = r2.l; //mac w2[0] * b2[1], store w2[0] to w2[1].  i1 now pointing to next channel. 
	r4 = (a0 += r2.h * r3.l) (is) || i0 -= 10; //mac w2[1] * b2[0], reset coeff. ptr, save in r4. 
	r4 = r4 >>> 14; 

here is a second (but still not final) attempt, once i realized that it is possible to issue 2 MACS per cycle

// I'm really happy with this - every cycle is doing two MMACs. :)
	//															i0	i1 (in 16 bit words)
	r1 = [i0++] || r4 = [i1++]; 						//	2	2 	r1= a0 a1 r4= w0's
	a0 = r0.l * r1.l, a1 = r0.h * r1.l || r2 = [i0++] || r5 = [i1]; 			//	4	2	r2= a2 a2 r5= w1's
	a0 += r4.l * r1.h, a1 = r4.h * r1.h  || r3 = [i0++] || [i1--] = r4; 		//	6	0	r3= b0 b1 w1's=r4
	r0.l = (a0 += r5.l * r2.l), r0.h = (a1 += r5.h * r2.l)(s2rnd); 
	a0 = r0.l * r3.l, a1 = r0.h * r3.l || [i1++] = r0; 					//	6	2	w0's = r0
	a0 += r4.l * r3.h, a1 += r4.h * r3.h || r1 = [i0++] || i1 += 4; 		//	8	4 	r1 = a0 a1 
	//load next a[0] a[1] to r1; move to next 2nd biquad w's; don't reset the coef pointer - move on to the next biquad. 
	r0.l = (a0 += r5.l * r3.l), r0.h = (a1 += r5.h * r3.l)(s2rnd) || r4 = [i1++]; //	8	6	r4 = w0's, next biquad
	//note: the s2rnd flag post-multiplies accumulator contents by 2.  see pg 581 or 15-69
	//second biquad. 
	a0 = r0.l * r1.l, a1 = r0.h * r1.l || r2 = [i0++] || r5 = [i1];			//	10	6	r2= a2 a2 r5 = w1's
	a0 += r4.l * r1.h, a1 += r4.h * r1.h || r3 = [i0++] || [i1--] = r4; 		//	12	4	r3= b0 b1 w1's = r4
	r0.l = (a0 += r5.l * r2.l), r0.h = (a1 += r5.h * r2.l)(s2rnd); 			//
	a0 = r0.l * r3.l, a1 = r0.h * r3.l || [i1++] = r0; 					//	12	6	w0's = r0
	a0 += r4.l * r3.h, a1 += r4.h * r3.h || r1 = [i0++] || i1 += 4; 		//	14	8	r1 = a0 a1
	r0.l = (a0 += r5.l * r3.l), r0.h = (a1 += r5.h * r3.l)(s2rnd) || r4 = [i1++]; //	14	10	r4 = w0's
	//third biquad. 
	a0 = r0.l * r1.l, a1 = r0.h * r1.l || r2 = [i0++] || r5 = [i1];			//	16	10	r2= a2 a2 r5 = w1's
	a0 += r4.l * r1.h, a1 += r4.h * r1.h || r3 = [i0++] || [i1--] = r4; 		//	18	8	r3= b0 b1 w1's = r4
	r0.l = (a0 += r5.l * r2.l), r0.h = (a1 += r5.h * r2.l)(s2rnd); 			//
	a0 = r0.l * r3.l, a1 = r0.h * r3.l || [i1++] = r0; 					//	18	10	w0's = r0
	a0 += r4.l * r3.h, a1 += r4.h * r3.h || r1 = [i0++] || i1 += 4; 		//	20	12	r1 = a0 a1
	r0.l = (a0 += r5.l * r3.l), r0.h = (a1 += r5.h * r3.l)(s2rnd) || r4 = [i1++]; //	20	14	r4 = w0's
	//fourth biquad. 
	a0 = r0.l * r1.l, a1 = r0.h * r1.l || r2 = [i0++] || r5 = [i1];			//	22	14
	a0 += r4.l * r1.h, a1 += r4.h * r1.h || r3 = [i0++] || [i1--] = r4; 		//	24	12
	r0.l = (a0 += r5.l * r2.l), r0.h = (a1 += r5.h * r2.l)(s2rnd); 
	a0 = r0.l * r3.l, a1 = r0.h * r3.l || [i1++] = r0; 					//	24	14
	a0 += r4.l * r3.h, a1 += r4.h * r3.h || i1 += 4; 					//	24	16
	r0.l = (a0 += r5.l * r3.l), r0.h = (a1 += r5.h * r3.l)(s2rnd); 			// 48: loop back; 32 bytes: move to next channel.

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ref: notes-0 tags: James DeMarsh PHF tlh24 Cornell date: 08-21-2007 16:35 gmt revision:0 [head]

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ref: notes-0 tags: pick place vacuum valves hypodermic PCB assembly date: 08-14-2007 04:01 gmt revision:2 [1] [0] [head]

For the past few days I've been trying to figure out a way to do vacuum-assisted pick & place for home SMT PCB assembly.

Fortunately, I had a vacuum pump - this one bought, without motor, from now defunct Duke University Surplus for $25. I got the motor from my parents, and had to go to Northern Equipment for the pulley and belt (fyi: also bought a hot air gun, Wel-Bilt brand, which promptly broke upon testing at home.) I filled it with 10w30 synthetic motor oil, since some of original vacuum oil had leaked out during the (years?) of neglect at the surplus store.

The whole assembly was far heavier than i could move, so i welded together a little cart for it out of old sideskate axles & bed frames. The wheels are from a cheap wal-mart skateboard that i used like 3 years ago to make the pogoboard. I also had to figure out how to neck down the 1.5" vacuum port on top of the huge pump to 1/4 id tubing, which took about 15 mins of searching in home depot...

Above - Valves clamped to the table so i can operate them with my right hand while my left hand manipulates the fine SMT devices. Top valve is to control the vacuum pressure, bottom is a dump valve to release vacuum in the tip.

I used three hypodermic needles, of varying diameter, as the tips for picking up small items. For the large chips, e.g. 176 pin LQFP-s, I ganked the ink tube out of a ballpoint pen. & glued it to the syringe connector (which is nice and easily replaceable). All tips were ground down at about a 40 deg angle to hold the parts (and to make the tips dull enough so i wouldn't continually stab myself while working...)

simple and effective!

see also {423}

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ref: notes-0 tags: blackfin BF537 memory map date: 08-01-2007 19:23 gmt revision:0 [head]

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ref: notes-0 tags: recording tech tbsi biosignal telemetry date: 05-20-2007 16:40 gmt revision:1 [0] [head]

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ref: notes-0 tags: RF telemetry differential phase shift key prosthesis power transmission TETS PSK date: 05-12-2007 23:13 gmt revision:0 [head]

transcutaneous data telemetry system tolerant to power telemetry interference

  • details optimum operation of class-E amplifier.
  • plus 1-2 MBaud data link, dual band to minimize interference.

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ref: notes-0 tags: Clementine review organize Miguel 042707 movies videos date: 04-29-2007 19:13 gmt revision:14 [13] [12] [11] [10] [9] [8] [head]

things that I want to send to miguel:

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ref: thesis-0 tags: clementine 042607 operant conditioning date: 04-27-2007 16:45 gmt revision:3 [2] [1] [0] [head]

tried 2d again... some success. looked at 29 (still good for x control, but not in BMI mode), channe 71 (still by default silent, correlated to behavior) channel 18 (did not work well) channel 84 (did not work) and channel 54 (like 71, highly correlated to behavior - not sure if the mk learned to control it). have videos etc.

channel 54, new for today and might, might be > 71.. though looking back at the videos, 71 seems pretty good. (it is also a bad idea to keep switching the game..) channels 54 and 71 are different from 29 in that 29 never goes completely silent; 71 goes silent when thew mk is paying attention, 54 when he is not moving. 29 can be modulated + and -, 71 and 54 just + (or so). of course, the monkey is usually in motion so both have high variance and silent periods are short-ish

channel 29, as always

channel 71, as before (very stable!)

channel 54

movies (in the order that they were taken):

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ref: thesis-0 tags: clementine 042507 operant conditioning date: 04-25-2007 20:19 gmt revision:2 [1] [0] [head]

OK, today clementine played absolutely abysmally - he did practically nothing, though he did do pole control for a little bit. I think we must stop doing pole control - it is too easy, he must become accustomed to doing brain control from the beginning. Anyway, monkeys never like learning new things (compare to people!); I just have to give him more time. The units are stable (in my agitated state, i forgot to make screenshots). Channel 54 might be very excellent for brain control - however, i did not test it today. If it is still there tomorrow, i will try.

http://m8ta.com/tim/clem042507_trainY.MPG (ignore the first few seconds - he was not trying so hard/was not paying attention)

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ref: thesis-0 tags: clementine 042407 operant conditioning date: 04-25-2007 00:21 gmt revision:1 [0] [head]

Today, as yesterday, I tried operantly conditioning primary units on channels 29 (x) and 71 (y) for BMI control. The first few minutes were run in pole control for Miguel's visitors, but i did not save the data. Again as before the monkey was not quite motivated to perform the task. Tomorrow he ought to be thirsty - & I'll try to start him on 2d control after tweaking the gain and offset parameters on the individual axes. During 2d control tomorrow the target size should be expanded also to about 3 to keep the monkey's interest.

There seems to be a bug in the BMI- when two units are sorted, both contribute to the firing rate estimate. I noticed this during X control today, which somewhat decreased the performance. Y performance was slightly better than yesterday, but still not great - he hasn't quite figured it out yet. XY was shitty, i guess.

Among other things, I really need to test the recording system - perhaps make a new file format that is extensible yet compressed? maybe labeled data streams? something like plexon files? Or perhaps just record it to the analog files (that would be easy!) nahh. todo:

  • write some matlab to combine the SQL records.
  • record the unit # in waveform record
  • save the logger output into the SQL db - not just in a file as now.
  • fix the onscreen shapes.

channel 29, at the end of the session:

channel 71. both these channels seem very stable - I hope the mk gets it before the evaporate!

there are no bmisql outputs as I did not run this analysis.


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ref: thesis-0 tags: clementine 042307 operant conditioning date: 04-24-2007 01:37 gmt revision:2 [1] [0] [head]

Today, once again, I tried BMI both via pole control and with operant conditioning. The latter worked the best; because the fit/predictions were so shitty i didn't even try brain control with the wiener filter or kalman filter. Here is the output of BMIsql on ~6500 data slices, 18 neurons, 5 taps:

here is the prediction summary... note that target x position is doing rather well (probably because we are training units to respond to this)

output of BMIsql:

order of columns: unit,channel, lag, snr, variable

    2.0000   29.0000         0    1.0872    6.0000
    1.0000   53.0000    3.0000    1.0870    3.0000
    1.0000   53.0000    2.0000    1.0820    3.0000
    1.0000   82.0000    1.0000    1.0801    7.0000
    1.0000   82.0000    5.0000    1.0678    1.0000
    1.0000   82.0000    4.0000    1.0625    1.0000
    1.0000   82.0000    2.0000    1.0563    7.0000
    1.0000   53.0000    1.0000    1.0558    6.0000
    1.0000    8.0000         0    1.0550    8.0000
    1.0000   70.0000    3.0000    1.0549    2.0000
    1.0000   70.0000    2.0000    1.0536    2.0000
    2.0000   82.0000    4.0000    1.0524    1.0000
    2.0000   82.0000    5.0000    1.0516    1.0000
    1.0000   53.0000    4.0000    1.0506    3.0000
    1.0000   70.0000    4.0000    1.0503    2.0000
    2.0000   29.0000    1.0000    1.0497    5.0000
    2.0000   82.0000    3.0000    1.0494    1.0000
    1.0000   82.0000    3.0000    1.0464    7.0000
    1.0000    8.0000    1.0000    1.0454    8.0000
    1.0000   24.0000    1.0000    1.0450    8.0000
    1.0000   24.0000         0    1.0442    8.0000
    1.0000    8.0000    2.0000    1.0415    8.0000
    1.0000   70.0000    5.0000    1.0396    2.0000
    2.0000   82.0000    1.0000    1.0395    7.0000
    1.0000   24.0000    2.0000    1.0392    8.0000
    1.0000   70.0000    1.0000    1.0389    2.0000
    1.0000   81.0000    1.0000    1.0356    8.0000
    1.0000    8.0000    3.0000    1.0355    8.0000
    2.0000   29.0000    2.0000    1.0334    8.0000
    1.0000   81.0000    2.0000    1.0326    8.0000
    1.0000   24.0000    4.0000    1.0318    8.0000
    1.0000    8.0000    4.0000    1.0298    8.0000
    1.0000   24.0000    3.0000    1.0297    8.0000
    1.0000   28.0000    3.0000    1.0293   11.0000
    2.0000   82.0000    2.0000    1.0292    4.0000
    1.0000   28.0000    1.0000    1.0286   11.0000
    1.0000   28.0000    4.0000    1.0262   11.0000
    1.0000   28.0000    2.0000    1.0243   11.0000
    1.0000   28.0000         0    1.0238   11.0000
    2.0000   29.0000    3.0000    1.0221    8.0000
    1.0000   53.0000         0    1.0215    9.0000
    1.0000   81.0000    3.0000    1.0207    8.0000

Operant conditioning worked exceptionally well for the X axis (channel 29, yellow unit 1 - adding both unit's activity together did not work, the monkey would not play). see http://m8ta.com/tim/clem042307_trainX.MPG For a while he tried controlling the cursor position with the joystick, then after a while he realized this was unnecessary and just modulated unit 29.

Initially I tried operant conditioning of channel 82 for the Y axis, but it quickly appeared that he did not care and that it would not work. Hence I switched to channel 71, which was tried on Saturday the 20th. As before, this unit was tonically active while he was asleep, and almost silent while he was paying attention. an attention neuron? possibly. It also showed high firing rate changes when he struggled, suggesting volitional control. He was somewhat able to control it today... see http://m8ta.com/tim/clem042307_trainY.MPG

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ref: Fromm-1981.05 tags: Evarts pyramidal tract size principle movements date: 04-23-2007 04:25 gmt revision:2 [1] [0] [head]

PMID-6809905[0] Relation of size and activity of motor cortex pyramidal tract neurons during skilled movements in the monkey

  • there did not seem to be a "size principle" in the strict sense that this term has been used with reference to spinal cord motoneurons.


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ref: Sergio-2005.1 tags: isometric motor control kinematics kinetics Kalaska date: 04-09-2007 22:33 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-15888522[0] Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks.

  • see [1]
  • recorded 132 units from the caudal M1
  • two tasks: isometric and movement of a heavy mass, both to 8 peripheral targets.
    • target location was digitized using a 'sonic digitizer'. trajectories look really good - the monkey was well trained.
  • idea: part of M1 functions near the output (of course)
    • evidence supporting this: M1 rasters during movement of the heavy mass show a triphasic profile: one to accelerate the mass, one to decelerate it, and another to hold it steady on target. see [2,3,4,5,6,7,8,9,10]


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ref: Caminiti-1991.05 tags: transform motor control M1 3D population_vector premotor Caminiti date: 04-09-2007 20:10 gmt revision:2 [1] [0] [head]

PMID-2027042[0] Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets.

  • trained monkeys to make similar movements in different parts of external/extrinsic 3D space.
  • change of preferred direction was graded in an orderly manner across extrinsic space.
  • virtually no correlations found to endpoint static position: "virtually all cells were related to the direction and not to the end point of movement" - compare to Graziano!
  • yet the population vector remained an accurate predictor of movement: "Unlike the individual cell preferred directions upon which they are based, movement population vectors did not change their spatial orientation across the work space, suggesting that they remain good predictors of movement direction regardless of the region of space in which movements are made"


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ref: Caminiti-1990.07 tags: transform motor control M1 3D population_vector premotor Caminiti date: 04-09-2007 20:07 gmt revision:4 [3] [2] [1] [0] [head]

PMID-2376768[0] Making arm movements within different parts of space: dynamic aspects in the primate motor cortex

  • monkeys made similar movements in different parts of external/extrinsic 3D space.
  • change of preferred direction was graded in an orderly manner across extrinsic space.
    • this change closely followed the changes in muscle activation required to effect the observed movements.
  • motor cortical cells can code direction of movement in a way which is dependent on the position of the arm in space
  • implies existence of mechanisms which facilitate the transformation between extrinsic (visual targets) and intrinsic coordinates
  • also see [1]


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ref: Townsend-2006.11 tags: EMG muscle activity dentate M1 cerebellum date: 04-09-2007 00:52 gmt revision:0 [head]

PMID-16790591[0] Linear encoding of muscle activity in primary motor cortex and cerebellum

  • precision grip task.
  • we showed that cells in both M1 and dentate encode muscle activity in a linear fashion
  • Neural activity in M1 was significantly more correlated with both EMG and kinematic signals than was activity in dentate nucleus
  • spike history effects added no information (probably due to the limited bandwidth of the output)


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ref: Fu-1993.11 tags: premotor M1 PMd PMv SUA date: 04-05-2007 17:12 gmt revision:0 [head]

PMID-8294972[0] Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys.

  • key thing: distance to target is represented in the motor & premotor corticies.


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ref: engineering-0 tags: schematic capture layout PCB design engineering date: 03-17-2007 23:44 gmt revision:0 [head]


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ref: physics notes-0 tags: plasma LDX dipole confinement fusion date: 02-24-2007 17:55 gmt revision:0 [head]

First Experiments to test plasma confinement by magnetic dipole

  • Beta is limited by the background pressure of lower-temperature gas - more gas = more stable & trapped electrons. presumably this is due to the presence of positive charges? I don't know, need to read more.
    • this is in the presence of 2-5Kw of microwave electron-cynchrotron radiation heating.
  • this is not levitated - it is the superconducting dipole held up with supports (steel cables? - looks pretty heavy!)
  • want to do catalyzed D-D fusion in the ultimate device
  • do say how they are going to get longer-term containment of the hot plasma.

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ref: Afanasev-2004.03 tags: striatum learning reinforcement electrophysiology putamen russians date: 02-05-2007 17:33 gmt revision:3 [2] [1] [0] [head]

PMID-15151178[0] Sequential Rearrangements of the Ensemble Activity of Putamen Neurons in the Monkey Brain as a Correlate of Continuous Behavior

  • recorded 6-7 neurons in the putamen during alternative spatial selection
  • used discriminant analysis (whats that?) to analyze re-arrangements in spike activity
  • dynamics of re-arrangnement were dependent on reinforcement, and mostly contralateral striatum


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ref: bookmark-0 tags: robot kinematics lagrangian dynamics date: 0-0-2007 0:0 revision:0 [head]


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ref: Barik-1996.1 tags: parkinsons dopamine cerebellum D3 essential tremor ET date: 0-0-2007 0:0 revision:0 [head]

  • PMID-8930390
    • There is a high concentration of dopamine in the 9 and 10 lobule of the cerebellum. quote: similar but weaker than the D3 response in the nucelus accumbens.
    • lobules 9 and 10 are involved in vestibular control of posture (?)
  • D3 is metabotropic inhibitory (sorta): molecular biology of the dopamine receptor subtypes
  • D3 is an autoreceptor; antagonism probably increases DA synaptic transmission.
    • Amisulpride is a D3 antagonist of the autoreceptor, and is used to treat the depressive elements at low doses(where it blocks autoreceptor) of schizophrenia at high doses (blocks postsynaptic recepor).
  • PMID-14622169 dopamine receptor expression is repressed in parkinsonian patients.
  • PMID-16809426 French patients with familial essential tremor are associated with polymorphisms in the D3 receptor gene.
    • a mutation which increases the affinity for dopamine causes an increase in the cAMP and MAPK response.
    • this mutation is harder to treat with parkinson's drugs - they suggest D3 antagonists for these patients of essential tremor.

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ref: notes-0 tags: essential tremor ET date: 0-0-2007 0:0 revision:0 [head]


  • essential tremor usually stops at night; hence, patients typically turn off the thalamic stimulator at night to conserve batteries.
  • in DBS the VIM (ventral intermediate) is targeted.
  • Blood flow responses to deep brain stimulation of thalamus
    • science is still unsure of the mechanism of DBS in VIM.
    • their PET scans indicated that DBS in VIM stimulates local blood flow, as well as blood flow in the SMA, which is innervated by VIM (quote: 'terminal fields of thalamocortical projections'). hence, the effect is activation, not inactivation.
  • http://neuroscienceupdate.cumc.columbia.edu/popups/transcript_pullman.html DBS of VIM also has been used in cerebellar outflow conditions as well
    • dementia is worstened by DBS :(
    • @ cornell they are doing genetically engineered transplantations to treat Parkinson's?
    • postural problems and balance issues don't improve with DBS in Parkinsons. (because of the cerebellum lobule 9-10?)
  • http://www.benbest.com/science/anatmind/anatmd7.html
    • VIM / VL thalamus projects to the supplementary motor area, and recieves input from the globus pallidus.
    • VP recieves input from the medial lemniscus, projects to S1 & somatosensory corticies.
    • cerebellum projects to the VA thalamus through the superior cerebellar peduncle.

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ref: bookmark-0 tags: neuroanatomy pulvinar thalamus superior colliculus image gray brainstem date: 0-0-2007 0:0 revision:0 [head]

http://en.wikipedia.org/wiki/Image:Gray719.png --great, very useful!

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ref: learning-0 tags: motor control primitives nonlinear feedback systems optimization date: 0-0-2007 0:0 revision:0 [head]

http://hardm.ath.cx:88/pdf/Schaal2003_LearningMotor.pdf not in pubmed.

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ref: GarciaRill-1991.01 tags: PPN pedunculopontine nucleus brainstem sleep locomotion consciousness 1991 date: 0-0-2007 0:0 revision:0 [head]

PMID-1887068 The Pedunculopontine nucleus

  • extensive review!

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ref: Wrosch-2007.02 tags: goals health 2007 disengagement date: 0-0-2007 0:0 revision:0 [head]

PMID-17259585 Giving up on unattainable goals: benefits for health?

  • Across the three studies, the findings demonstrate that the ability to disengage from unattainable goals is associated with better self-reported health and more normative patterns of diurnal cortisol secretion.
  • Goal reengagement, by contrast, was unrelated to indicators of physical health
  • PMID-17259584 Does self-affirmation, cognitive processing, or discovery of meaning explain cancer-related health benefits of expressive writing? quote: A content analysis of the essays showed that self-affirmation writing was associated with fewer physical symptoms at a 3-month follow-up assessment

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ref: bookmark-0 tags: neural_networks machine_learning matlab toolbox supervised_learning PCA perceptron SOM EM date: 0-0-2006 0:0 revision:0 [head]

http://www.ncrg.aston.ac.uk/netlab/index.php n.b. kinda old. (or does that just mean well established?)

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ref: bookmark-0 tags: job_search professional employment wisdom date: 0-0-2006 0:0 revision:0 [head]


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ref: bookmark-0 tags: muscle artifial catalyst nanotubes shape-memory alloy date: 0-0-2006 0:0 revision:0 [head]


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ref: bookmark-0 tags: Linux device_drivers memory virtual_memory PCI address_translation date: 0-0-2006 0:0 revision:0 [head]


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ref: abstract-0 tags: tlh24 error signals in the cortex and basal ganglia reinforcement_learning gradient_descent motor_learning date: 0-0-2006 0:0 revision:0 [head]

Title: Error signals in the cortex and basal ganglia.

Abstract: Numerous studies have found correlations between measures of neural activity, from single unit recordings to aggregate measures such as EEG, to motor behavior. Two general themes have emerged from this research: neurons are generally broadly tuned and are often arrayed in spatial maps. It is hypothesized that these are two features of a larger hierarchal structure of spatial and temporal transforms that allow mappings to procure complex behaviors from abstract goals, or similarly, complex sensory information to produce simple percepts. Much theoretical work has proved the suitability of this organization to both generate behavior and extract relevant information from the world. It is generally agreed that most transforms enacted by the cortex and basal ganglia are learned rather than genetically encoded. Therefore, it is the characterization of the learning process that describes the computational nature of the brain; the descriptions of the basis functions themselves are more descriptive of the brain’s environment. Here we hypothesize that learning in the mammalian brain is a stochastic maximization of reward and transform predictability, and a minimization of transform complexity and latency. It is probable that the optimizations employed in learning include both components of gradient descent and competitive elimination, which are two large classes of algorithms explored extensively in the field of machine learning. The former method requires the existence of a vectoral error signal, while the latter is less restrictive, and requires at least a scalar evaluator. We will look for the existence of candidate error or evaluator signals in the cortex and basal ganglia during force-field learning where the motor error is task-relevant and explicitly provided to the subject. By simultaneously recording large populations of neurons from multiple brain areas we can probe the existence of error or evaluator signals by measuring the stochastic relationship and predictive ability of neural activity to the provided error signal. From this data we will also be able to track dependence of neural tuning trajectory on trial-by-trial success; if the cortex operates under minimization principles, then tuning change will have a temporal relationship to reward. The overarching goal of this research is to look for one aspect of motor learning – the error signal – with the hope of using this data to better understand the normal function of the cortex and basal ganglia, and how this normal function is related to the symptoms caused by disease and lesions of the brain.

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ref: notes-0 tags: praxis movement vision date: 0-0-2006 0:0 revision:0 [head]

  • praxis is the ability to plan and sequence unfamiliar actions.
  • things are only remembered when they produce motor output.
  • if you put an eye patch over one eye in a 2-year old human for two weeks, the child's vision in that eye will be permanently impared.
  • movement stimulates creativity.