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{1576} |
ref: -0
tags: GFlowNet Bengio probabilty modelling reinforcement learing
date: 10-29-2023 19:17 gmt
revision:3
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{1575} | |||||||||||||||||||||||||||||||
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{1574} |
ref: -0
tags: ocaml application functional programming
date: 10-11-2022 21:36 gmt
revision:2
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https://stackoverflow.com/questions/26475765/ocaml-function-with-variable-number-of-arguments From this I learned that in ocaml you can return not just functions (e.g. currying) but appliations of yet-to-be named functions. let sum f = f 0 ;; let arg a b c = c ( b + a ) ;; let z a = a ;; then sum (arg 1) ;; is well-typed as (int -> `a) -> `a = <fun> e.g. an application of a function that converts int to `a. Think of it as the application of Xa to argument ( 0 + 1 ), where Xa is the argument (per type signature). Zero is supplied by the definition of 'sum'. sum (arg 1) (arg 2);; can be parsed as (sum (arg 1)) (arg 2) ;; '(arg 2)' outputs an application of an int & a yet-to be determined function to 'a, E.g. it's typed as int -> (int -> `a) -> `a = <fun>. So, you can call it Xa passed to above. Or, Xa = Xb( ( 0 + 1 ) + 2) where, again, Xb is a yet-to-be defined function that is supplied as an argument. Therefore, you can collapse the whole chain with the identity function z. But, of course, it could be anything else -- square root perhaps for MSE? All very clever. | |||||||||||||||||||||||||||||||
{1571} | |||||||||||||||||||||||||||||||
One model for the learning of language
A more interesting result is Deep symbolic regression for recurrent sequences, where the authors (facebook/meta) use a Transformer -- in this case, directly taken from Vaswini 2017 (8-head, 8-layer QKV w/ a latent dimension of 512) to do both symbolic (estimate the algebraic recurrence relation) and numeric (estimate the rest of the sequence) training / evaluation. Symbolic regression generalizes better, unsurprisingly. But both can be made to work even in the presence of (log-scaled) noise! While the language learning paper shows that small generative programs can be inferred from a few samples, the Meta symbolic regression shows that Transformers can evince either amortized memory (less likely) or algorithms for perception -- both new and interesting. It suggests that 'even' abstract symbolic learning tasks are sufficiently decomposable that the sorts of algorithms available to an 8-layer transformer can give a useful search heuristic. (N.B. That the transformer doesn't spit out perfect symbolic or numerical results directly -- it also needs post-processing search. Also, the transformer algorithm has search (in the form of softmax) baked in to it's architecture.) This is not a light architecture: they trained the transformer for 250 epochs, where each epoch was 5M equations in batches of 512. Each epoch took 1 hour on 16 Volta GPUs w 32GB of memory. So, 4k GPU-hours x ~10 TFlops = 1.4e20 Flops. Compare this with grammar learning above; 7 days on 32 cores operating at ~ 3Gops/sec is 1.8e15 ops. Much, much smaller compute. All of this is to suggest a central theme of computer science: a continuum between search and memorization.
Most interesting for a visual neuroscientist (not that I'm one per se, but bear with me) is where on these axes (search, heuristic, memory) visual perception is. Clearly there is a high degree of recurrence, and a high degree of plasticity / learning. But is there search or local optimization? Is this coupled to the recurrence via some form of energy-minimizing system? Is recurrence approximating E-M? | |||||||||||||||||||||||||||||||
{1568} | |||||||||||||||||||||||||||||||
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
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{1564} | |||||||||||||||||||||||||||||||
“Visualizing data using t-SNE”
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{842} | |||||||||||||||||||||||||||||||
Distilling free-form natural laws from experimental data
Since his Phd, Michael Schmidt has gone on to found Nutonian, which produced Eurequa software, apparently without dramatic new features other than being able to use the cloud for equation search. (Probably he improved many other detailed facets of the software..). Nutonian received $4M in seed funding, according to Crunchbase. In 2017, Nutonian was acquired by Data Robot (for an undisclosed amount), where Michael has worked since, rising to the title of CTO. Always interesting to follow up on the authors of these classic papers! | |||||||||||||||||||||||||||||||
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The fact that sVD works at all, and pulls out some structure is interesting! Not nearly as good as word2vec. | |||||||||||||||||||||||||||||||
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The HSIC Bottleneck: Deep learning without Back-propagation In this work, the authors use a kernelized estimate of statistical independence as part of a 'information bottleneck' to set per-layer objective functions for learning useful features in a deep network. They use the HSIC, or Hilbert-schmidt independence criterion, as the independence measure. The information bottleneck was proposed by Bailek (spikes..) et al in 1999, and aims to increase the mutual information between the layer representation and the labels while minimizing the mutual information between the representation and the input:
Where is the hidden representation at layer i (later output), is the layer input, and are the labels. By replacing with the HSIC, and some derivation (?), they show that
Where are samples and labels, and -- that is, it's the kernel function applied to all pairs of (vectoral) input variables. H is the centering matrix. The kernel is simply a Gaussian kernel, . So, if all the x and y are on average independent, then the inner-product will be mean zero, the kernel will be mean one, and after centering will lead to zero trace. If the inner product is large within the realm of the derivative of the kernel, then the HSIC will be large (and negative, i think). In practice they use three different widths for their kernel, and they also center the kernel matrices. But still, the feedback is an aggregate measure (the trace) of the product of two kernelized (a nonlinearity) outer-product spaces of similarities between inputs. it's not unimaginable that feedback networks could be doing something like this... For example, a neural network could calculate & communicate aspects of joint statistics to reward / penalize weights within a layer of a network, and this is parallelizable / per layer / adaptable to an unsupervised learning regime. Indeed, that was done almost exactly by this paper: Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks albeit in a much less intelligible way. Robust Learning with the Hilbert-Schmidt Independence Criterion Is another, later, paper using the HSIC. Their interpretation: "This loss-function encourages learning models where the distribution of the residuals between the label and the model prediction is statistically independent of the distribution of the instances themselves." Hence, given above nomenclature, (I'm not totally sure about the weighting, but might be required given the definition of the HSIC.) As I understand it, the HSIC loss is a kernellized loss between the input, output, and labels that encourages a degree of invariance to input ('covariate shift'). This is useful, but I'm unconvinced that making the layer output independent of the input is absolutely essential (??) | |||||||||||||||||||||||||||||||
{1552} | |||||||||||||||||||||||||||||||
Modularizing Deep Learning via Pairwise Learning With Kernels
I think in general this is an important result, even if its not wholly unique / somewhat anticipated (it's a year old at the time of writing). Modular training of neural networks is great for efficiency, parallelization, and biological implementations! Transport of weights between layers is hence non-essential. Classes still are, but I wonder if temporal continuity can solve some of these problems? (There is plenty of other effort in this area -- see also {1544}) | |||||||||||||||||||||||||||||||
{1547} | |||||||||||||||||||||||||||||||
Meta-Learning Update Rules for Unsupervised Representation Learning
This is a clearly-written, easy to understand paper. The results are not highly compelling, but as a first set of experiments, it's successful enough. I wonder what more constraints (fewer parameters, per the genome), more options for architecture modifications (e.g. different feedback schemes, per neurobiology), and a black-box optimization algorithm (evolution) would do? | |||||||||||||||||||||||||||||||
{1449} | |||||||||||||||||||||||||||||||
This was compiled from searching papers which referenced Olshausen and Field 1996 PMID-8637596 Emergence of simple-cell receptive field properties by learning a sparse code for natural images.
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{1546} | |||||||||||||||||||||||||||||||
Local synaptic learning rules suffice to maximize mutual information in a linear network
x = randn(1000, 10); Q = x' * x; a = 0.001; Y = randn(10, 1); y = zeros(10, 1); for i = 1:1000 y = Y + (eye(10) - a*Q)*y; end y - pinv(Q)*Y / a % should be zero.
To this is added a 'sensing' learning and 'noise' unlearning phase -- one optimizes , the other minimizes . Everything is then applied, similar to before, to a gaussian-filtered one-dimensional white-noise stimuli. He shows this results in bandpass filter behavior -- quite weak sauce in an era where ML papers are expected to test on five or so datasets. Even if this was 1992 (nearly forty years ago!), it would have been nice to see this applied to a more realistic dataset; perhaps some of the following papers? Olshausen & Field came out in 1996 -- but they applied their algorithm to real images. In both Olshausen & this work, no affordances are made for multiple layers. There have to be solutions out there... | |||||||||||||||||||||||||||||||
{1545} | |||||||||||||||||||||||||||||||
Self-organizaton in a perceptual network
One may critically challenge the infomax idea: we very much need to (and do) throw away spurious or irrelevant information in our sensory streams; what upper layers 'care about' when making decisions is certainly relevant to the lower layers. This credit-assignment is neatly solved by backprop, and there are a number 'biologically plausible' means of performing it, but both this and infomax are maybe avoiding the problem. What might the upper layers really care about? Likely 'care about' is an emergent property of the interacting local learning rules and network structure. Can you search directly in these domains, within biological limits, and motivated by statistical reality, to find unsupervised-learning networks? You'll still need a way to rank the networks, hence an objective 'care about' function. Sigh. Either way, I don't per se put a lot of weight in the infomax principle. It could be useful, but is only part of the story. Otherwise Linsker's discussion is accessible, lucid, and prescient. Lol. | |||||||||||||||||||||||||||||||
{1543} |
ref: -2019
tags: backprop neural networks deep learning coordinate descent alternating minimization
date: 07-21-2021 03:07 gmt
revision:1
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Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
This is interesting in that the weight updates can be cone in parallel - perhaps more efficient - but you are still propagating errors backward, albeit via optimizing 'codes'. Given the vast infractructure devoted to auto-diff + backprop, I can't see this being adopted broadly. That said, the idea of alternating minimization (which is used eg for EM clustering) is powerful, and this paper does describe (though I didn't read it) how there are guarantees on the convexity of the alternating minimization. Likewise, the authors show how to improve the performance of the online / minibatch algorithm by keeping around memory variables, in the form of covariance matrices. | |||||||||||||||||||||||||||||||
{1541} | |||||||||||||||||||||||||||||||
Like this blog but 100% better! | |||||||||||||||||||||||||||||||
{1540} | |||||||||||||||||||||||||||||||
Two Routes to Scalable Credit Assignment without Weight Symmetry This paper looks at five different learning rules, three purely local, and two non-local, to see if they can work as well as backprop in training a deep convolutional net on ImageNet. The local learning networks all feature forward weights W and backward weights B; the forward weights (+ nonlinearities) pass the information to lead to a classification; the backward weights pass the error, which is used to locally adjust the forward weights. Hence, each fake neuron has locally the forward activation, the backward error (or loss gradient), the forward weight, backward weight, and Hebbian terms thereof (e.g the outer product of the in-out vectors for both forward and backward passes). From these available variables, they construct the local learning rules:
Each of these serves as a "regularizer term" on the feedback weights, which governs their learning dynamics. In the case of backprop, the backward weights B are just the instantaneous transpose of the forward weights W. A good local learning rule approximates this transpose progressively. They show that, with proper hyperparameter setting, this does indeed work nearly as well as backprop when training a ResNet-18 network. But, hyperparameter settings don't translate to other network topologies. To allow this, they add in non-local learning rules:
In "Symmetric Alignment", the Self and Decay rules are employed. This is similar to backprop (the backward weights will track the forward ones) with L2 regularization, which is not new. It performs very similarly to backprop. In "Activation Alignment", Amp and Sparse rules are employed. I assume this is supposed to be more biologically plausible -- the Hebbian term can track the forward weights, while the Sparse rule regularizes and stabilizes the learning, such that overall dynamics allow the gradient to flow even if W and B aren't transposes of each other. Surprisingly, they find that Symmetric Alignment to be more robust to the injection of Gaussian noise during training than backprop. Both SA and AA achieve similar accuracies on the ResNet benchmark. The authors then go on to explain the plausibility of non-local but approximate learning rules with Regression discontinuity design ala Spiking allows neurons to estimate their causal effect. This is a decent paper,reasonably well written. They thought trough what variables are available to affect learning, and parameterized five combinations that work. Could they have done the full matrix of combinations, optimizing just they same as the metaparameters? Perhaps, but that would be even more work ... Regarding the desire to reconcile backprop and biology, this paper does not bring us much (if at all) closer. Biological neural networks have specific and local uses for error; even invoking 'error' has limited explanatory power on activity. Learning and firing dynamics, of course of course. Is the brain then just an overbearing mess of details and overlapping rules? Yes probably but that doesn't mean that we human's can't find something simpler that works. The algorithms in this paper, for example, are well described by a bit of linear algebra, and yet they are performant. | |||||||||||||||||||||||||||||||
{1538} | |||||||||||||||||||||||||||||||
PMID-20596024 Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex
Cortical reliability amid noise and chaos
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{1537} |
ref: -0
tags: cortical computation learning predictive coding reviews
date: 02-23-2021 20:15 gmt
revision:2
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PMID-30359606 Predictive Processing: A Canonical Cortical Computation
PMID-23177956 Canonical microcircuits for predictive coding
Control of synaptic plasticity in deep cortical networks
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From Protein Structure to Function with Bioinformatics
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PMID-23273272 A cellular mechanism for cortical associations: and organizing principle for the cerebral cortex
See also: PMID-25174710 Sensory-evoked LTP driven by dendritic plateau potentials in vivo
And: The binding solution?, a blog post covering Bittner 2015 that looks at rapid dendritic plasticity in the hippocampus as a means of binding stimuli to place fields. | |||||||||||||||||||||||||||||||
{1523} |
ref: -0
tags: tennenbaum compositional learning character recognition one-shot learning
date: 02-23-2021 18:56 gmt
revision:2
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One-shot learning by inverting a compositional causal process
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{1534} | |||||||||||||||||||||||||||||||
Going in circles is the way forward: the role of recurrence in visual inference I think the best part of this article are the references -- a nicely complete listing of, well, the current opinion in Neurobiology! (Note that this issue is edited by our own Karel Svoboda, hence there are a good number of Janelians in the author list..) The gestalt of the review is that deep neural networks need to be recurrent, not purely feed-forward. This results in savings in overall network size, and increase in the achievable computational complexity, perhaps via the incorporation of priors and temporal-spatial information. All this again makes perfect sense and matches my sense of prevailing opinion. Of course, we are left wanting more: all this recurrence ought to be structured in some way. To me, a rather naive way of thinking about it is that feed-forward layers cause weak activations, which are 'amplified' or 'selected for' in downstream neurons. These neurons proximally code for 'causes' or local reasons, based on the supported hypothesis that the brain has a good temporal-spatial model of the visuo-motor world. The causes then can either explain away the visual input, leading to balanced E-I, or fail to explain it, in which the excess activity is either rectified by engaging more circuits or engaging synaptic plasticity. A critical part of this hypothesis is some degree of binding / disentanglement / spatio-temporal re-assignment. While not all models of computation require registers / variables -- RNNs are Turning-complete, e.g., I remain stuck on the idea that, to explain phenomenological experience and practical cognition, the brain much have some means of 'binding'. A reasonable place to look is the apical tuft dendrites, which are capable of storing temporary state (calcium spikes, NMDA spikes), undergo rapid synaptic plasticity, and are so dense that they can reasonably store the outer-product space of binding. There is mounting evidence for apical tufts working independently / in parallel is investigations of high-gamma in ECoG: PMID-32851172 Dissociation of broadband high-frequency activity and neuronal firing in the neocortex. "High gamma" shows little correlation with MUA when you differentiate early-deep and late-superficial responses, "consistent with the view it reflects dendritic processing separable from local neuronal firing" | |||||||||||||||||||||||||||||||
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PMID-24204224 The Convallis rule for unsupervised learning in cortical networks 2013 - Pierre Yger 1 , Kenneth D Harris This paper aims to unify and reconcile experimental evidence of in-vivo learning rules with established STDP rules. In particular, the STDP rule fails to accurately predict change in strength in response to spike triplets, e.g. pre-post-pre or post-pre-post. Their model instead involves the competition between two time-constant threshold circuits / coincidence detectors, one which controls LTD and another LTP, and is such an extension of the classical BCM rule. (BCM: inputs below a threshold will weaken a synapse; those above it will strengthen. ) They derive the model from optimization criteria that neurons should try to optimize the skewedness of the distribution of their membrane potential: much time spent either firing spikes or strongly inhibited. This maps to a objective function F that looks like a valley - hence the 'convallis' in the name (latin for valley); the objective is differentiated to yield a weighting function for weight changes; they also add a shrinkage function (line + heaviside function) to gate weight changes 'off' at resting membrane potential. A network of firing neurons successfully groups correlated rate-encoded inputs, better than the STDP rule. it can also cluster auditory inputs of spoken digits converted into cochleogram. But this all seems relatively toy-like: of course algorithms can associate inputs that co-occur. The same result was found for a recurrent balanced E-I network with the same cochleogram, and convalis performed better than STDP.  Meh. Perhaps the biggest thing I got from the paper was how poorly STDP fares with spike triplets: Pre following post does not 'necessarily' cause LTD; it's more complicated than that, and more consistent with the two different-timeconstant coincidence detectors. This is satisfying as it allows for apical dendritic depolarization to serve as a contextual binding signal - without negatively impacting the associated synaptic weights. | |||||||||||||||||||||||||||||||
{1527} |
ref: -0
tags: inductive logic programming deepmind formal propositions prolog
date: 11-21-2020 04:07 gmt
revision:0
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Learning Explanatory Rules from Noisy Data
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{1490} | |||||||||||||||||||||||||||||||
PMID-21527931 Two-photon absorption properties of fluorescent proteins
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Schema networks: zero-shot transfer with a generative causal model of intuitive physics
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PMID-15142952 Visual binding through reentrant connectivity and dynamic synchronization in a brain-based device
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{1417} |
ref: -0
tags: synaptic plasticity 2-photon imaging inhibition excitation spines dendrites synapses 2p
date: 08-14-2020 01:35 gmt
revision:3
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PMID-22542188 Clustered dynamics of inhibitory synapses and dendritic spines in the adult neocortex.
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PMID-31780899 Single Synapse LTP: A matter of context?
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PMID-26621426 Causal Inference and Explaining Away in a Spiking Network
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Super-Photostable Phosphole-Based Dye for Multiple-Acquisition Stimulated Emission Depletion Imaging
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{1500} | |||||||||||||||||||||||||||||||
PMID-31942076 A distributional code for value in dopamine based reinforcement learning
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Scalable and sustainable deep learning via randomized hashing
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Finding frequent items in data streams
Mission: Ultra large-scale feature selection using Count-Sketches
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A 6-nm ultra-photostable DNA Fluorocube for fluorescence imaging Also including some correspondence with the authors: Me Nice work and nice paper, thanks for sharing .. and not at all what I had expected from Ron's comments! Below are some comments ... would love your opinion. I'd expect that the molar absorption coefficients for the fluorocubes should be ~6x larger than for the free dyes and the single dye cubes (measured?), yet the photon yields for all except Cy3N maybe are around the yield for one dye molecule. So the quantum yield must be decreased by ~6x? This in turn might be from a middling FRET which reduces lifetime, thereby the probability of ISC, photoelectron transfer, and hence photobleaching. I wonder if in the case of ATTO 647N Cy5 and Cy3, the DNA is partly shielding the fluorphores from solvent (ala ethidium bromide), which also helps with stability, just like in fluorescent proteins. ATTO 647N generates a lot of singlet oxygen, who knows what it's doing to DNA. Can you do a log-log autocorrelation of the blinking timeseries of the constructs? This may reveal different rate constants controlling dark/light states (though, for 6 coupled objects, might not be interpretable!) Also, given the effect of DNA shielding, have you compared to free dyes to single-dye cubes other than supp fig 10? The fact that sulfonation made such a huge effect in brightness is suggestive. Again, these are super interesting & exciting results! Author I haven't directly looked at the molar absorption coefficient but judging from the data that I collected for the absorption spectra, there is certainly an increase for the fluorocubes compared to single dyes. I agree that this would be an interesting experiment and I am planning collect data to measure the molar absorption coefficient. I would also expect a ~6 fold increase for the Fluorocubes. Yes, we suspect homo FRET to help reduce photobleaching. So far we only measured lifetimes in bulk but are planning to obtain lifetime data on the single-molecule level soon. We also wondered if the DNA is providing some kind of shield for the fluorophores but could not design an experiment to directly test this hypothesis. If you have a suggestion, that would be wonderful. The log-log autocorrelation of blinking events is indeed difficult to interpret. Already individual intensity traces of fluorocubes are difficult to analyze as many of them get brighter before they bleach. We are also wondering if some fluorocubes are emitting two photons simultaneously. We will hopefully be able to measure this soon. | |||||||||||||||||||||||||||||||
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PMID-18204458 High-speed, low-photodamage nonlinear imaging using passive pulse splitters
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Accelerated FRET-PAINT Microscopy
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PMID-24877017 Optimal lens design and use in laser-scanning microscopy
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Why multifactor?
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PMID-26659050 Human level concept learning through probabalistic program induction
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PMID-29123069 A neural algorithm for a fundamental computing problem
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PMID-27690349 Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation
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{1492} | |||||||||||||||||||||||||||||||
PMID: Spiking neurons can discover predictive features by aggregate-label learning
Editorializing a bit: I said this was interesting, but why? The first part of the paper is another form of SGD, albeit in a spiking neural network, where the gradient is harder compute hence is done numerically. It's the aggregate part that is new -- pulling in repeated patterns through synaptic learning rules. Of course, to do this, the full trace of pre and post synaptic activity must be recorded (??) for estimating the STS (i think). An eligibility trace moves in the right direction as a biologically plausible approximation, but as always nothing matches the precision of SGD. Can the eligibility trace be amended with e.g. neuromodulators to push the performance near that of SGD? The next step of adding self supervised singular and multiple features is perhaps toward the way the brain organizes itself -- small local feedback loops. These features annotate repeated occurrences of stimuli, or tile a continuous feature space. Still, the fact that I haven't seen any follow-up work is suggestive... Editorializing further, there is a limited quantity of work that a single human can do. In this paper, it's a great deal of work, no doubt, and the author offers some good intuitions for the design decisions. Yet still, the total complexity that even a very determined individual can amass is limited, and likely far below the structural complexity of a mammalian brain. This implies that inference either must be distributed and compositional (the normal path of science), or the process of evaluating & constraining models must be significantly accelerated. This later option is appealing, as current progress in neuroscience seems highly technology limited -- old results become less meaningful when the next wave of measurement tools comes around, irrespective of how much work went into it. (Though: the impedtus for measuring a particular thing in biology is only discovered through these 'less meaningful' studies...). A third option, perhaps one which many theoretical neuroscientists believe in, is that there are some broader, physics-level organizing principles to the brain. Karl Friston's free energy principle is a good example of this. Perhaps at a meta level some organizing theory can be found, or likely a set of theories; but IMHO, you'll need at least one theory per brain area, at least, just the same as each area is morphologically, cytoarchitecturaly, and topologically distinct. (There may be only a few theories of the cortex, despite all the areas, which is why so many are eager to investigate it!) So what constitutes a theory? Well, you have to meaningfully describe what a brain region does. (Why is almost as important; how more important to the path there.) From a sensory standpoint: what information is stored? What processing gain is enacted? How does the stored information impress itself on behavior? From a motor standpoint: how are goals selected? How are the behavioral segments to attain them sequenced? Is the goal / behavior even a reasonable way of factoring the problem? Our dual problem, building the bridge from the other direction, is perhaps easier. Or it could be a lot more money has gone into it. Either way, much progress has been made in AI. One arm is deep function approximation / database compression for fast and organized indexing, aka deep learning. Many people are thinking about that; no need to add to the pile; anyway, as OpenAI has proven, the common solution to many problems is to simply throw more compute at it. A second is deep reinforcement learning, which is hideously sample and path inefficient, hence ripe for improvement. One side is motor: rather than indexing raw motor variables (LRUD in a video game, or joint torques with a robot..) you can index motor primitives, perhaps hierarchically built; likewise, for the sensory input, the model needs to infer structure about the world. This inference should decompose overwhelming sensory experience into navigable causes ... But how can we do this decomposition? The cortex is more than adept at it, but now we're at the original problem, one that the paper above purports to make a stab at. | |||||||||||||||||||||||||||||||
{1491} | |||||||||||||||||||||||||||||||
PMID-29853555 Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors
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{1489} | |||||||||||||||||||||||||||||||
{1488} | |||||||||||||||||||||||||||||||
PMID-30588295 Subcellular spatial resolution achieved for deep-brain imaging in vivo using a minimally invasive multimode fiber | |||||||||||||||||||||||||||||||
{1487} |
ref: -0
tags: adaptive optics sensorless retina fluorescence imaging optimization zernicke polynomials
date: 11-15-2019 02:51 gmt
revision:0
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PMID-26819812 Wavefront sensorless adaptive optics fluorescence biomicroscope for in vivo retinal imaging in mice
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{1485} | |||||||||||||||||||||||||||||||
PMID-26352471 Labelling and optical erasure of synaptic memory traces in the motor cortex
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{1482} | |||||||||||||||||||||||||||||||
Rapid learning or feature reuse? Towards understanding the effectiveness of MAML
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{1479} | |||||||||||||||||||||||||||||||
Can we image biological tissue with entangled photons? How much fluorescence can we expect, based on reasonable concentrations & published ETPA cross sections? Start with beer's law: = absorbance; = sample length, 10 μm, 1e-3 cm; = concentration, 10 μmol; = cross-section, for ETPA assume (this is based on a FMN based fluorophore; actual cross-section may be higher). Including Avogadro's number and , Now, add in quantum efficiency (Rhodamine); collection efficiency ; and an incoming photon pair flux of (which roughly about the limit for quantum behavior; n = 0.1 photons / mode; will add this calculation). This is very low, but within practical imaging limits. As a comparison, incoherent 2p imaging creates ~ 100 photons per pulse, of which 10 make it to the detector; for 512 x 512 pixels at 15fps, the dwell time on each pixel is 20 pulses of a 80 MHz Ti:Sapphire laser, or ~ 200 photons. Note the pair flux is per optical mode; for a typical application, we'll use a Nikon 16x objective with a 600 μm Ø FOV and 0.8 NA. At 800 nm imaging wavelength, the diffraction limit is 0.5 μm. This equates to about addressable modes in the FOV. Then an illumination of photons / sec / mode equates to photons over the whole field; if each photon pair has an energy of , this is equivalent to 300 mW. 100mW is a reasonable limit, hence scale incoming flux to pairs /sec. Hence, the imaging mode is power limited, and not quantum limited (if you could get such a bright entangled source). And right now that's the limit -- for a BBO crystal, circa 1998 experimenters were getting 1e4 photons / sec / mW. So, pairs / sec would require 23 GW. Yikes. More efficient entangled sources have been developed, using periodically-poled potassium titanyl phosphate (PPPTP), which (again assuming linearity) puts the power requirement at 23 MW. This is within the reason of q-switched lasers, but still incredibly inefficient. The down-conversion process is not linear in intensity, which is why Goodson pumps with SHG from a Ti:sapphire to yield ~1e7 photons; but this of induces temporal correlations which increase the frequency of incoherent TPA. Still, combining PPPTP with a Ti:sapphire laser could result in 1e13 photons / sec, which is sufficient for scanned microscopy. Since the laser is pulsed, it will still be subject to incoherent TPA; but that's OK, the point is to reduce the power going into the animal via larger ETPA cross-section. The answer to above is a tentative yes. Upon the development of brighter entangled sources (e.g. arrays of quantum structures), this can move to fully widefield imaging. | |||||||||||||||||||||||||||||||
{1474} | |||||||||||||||||||||||||||||||
Various papers put out by the Goodson group:
And from a separate group at Northwestern:
Regarding high fluence sources, quantum dots / quantum structures seem promising. | |||||||||||||||||||||||||||||||
{208} | |||||||||||||||||||||||||||||||
PMID-22388818 Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills.
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{1475} |
ref: -2017
tags: two photon holographic imaging Arch optogenetics GCaMP6
date: 09-12-2019 19:24 gmt
revision:1
[0] [head]
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PMID-28053310 Simultaneous high-speed imaging and optogenetic inhibition in the intact mouse brain.
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{1473} | |||||||||||||||||||||||||||||||
PMID-17179937 Major signal increase in fluorescence microscopy through dark-state relaxation (2007)
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{1472} |
ref: -0
tags: computational neuroscience opinion tony zador konrad kording lillicrap
date: 07-30-2019 21:04 gmt
revision:0
[head]
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Two papers out recently in Arxive and Biorxiv:
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{1470} | |||||||||||||||||||||||||||||||
Large-Scale Optical Neural Networks based on Photoelectric Multiplication
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{1469} |
ref: -2016
tags: fluorescent proteins photobleaching quantum yield piston GFP
date: 06-19-2019 14:33 gmt
revision:0
[head]
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PMID-27240257 Quantitative assessment of fluorescent proteins.
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{1467} |
ref: -2017
tags: neuromorphic optical computing nanophotonics
date: 06-17-2019 14:46 gmt
revision:5
[4] [3] [2] [1] [0] [head]
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Progress in neuromorphic photonics
See also :
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{1468} |
ref: -2013
tags: microscopy space bandwidth product imaging resolution UCSF
date: 06-17-2019 14:45 gmt
revision:0
[head]
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How much information does your microscope transmit?
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{1464} | |||||||||||||||||||||||||||||||
Nanoelectronic Programmable Synapses Based on Phase Change Materials for Brain-Inspired Computing
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{1463} | |||||||||||||||||||||||||||||||
All-optical spiking neurosynaptic networks with self-learning capabilities
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{1460} | |||||||||||||||||||||||||||||||
A friend postulated using the triplet state phosphorescence as a magnetically-modulatable dye. E.g. magnetically slice a scattering biological sample, rather than slicing optically (light sheet, 2p) or mechanically. After a little digging:
I'd imagine that it should be possible to design a molecule -- a protein cage, perhaps a (fully unsaturated) terpine -- which isolates the excited state from oxygen quenching. Adam Cohen at Harvard has been working a bit on this very idea, albeit with fluorescence not phosphorescence --
Yet! Magnetic field effects do exist in solution:
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{1459} |
ref: -2018
tags: Michael Levin youtube talk NIPS 2018 regeneration bioelectricity organism patterning flatworm
date: 04-09-2019 18:50 gmt
revision:1
[0] [head]
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What Bodies Think About: Bioelectric Computation Outside the Nervous System - NeurIPS 2018
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{1458} | |||||||||||||||||||||||||||||||
PMID-28739915 Interactions between feedback and lateral connections in the primary visual cortex
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{1457} | |||||||||||||||||||||||||||||||
PMID-25112683 Subcellular Neural Probes from Single-Crystal Gold Nanowires
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{1456} | |||||||||||||||||||||||||||||||
PMID-21360044 Robust penetrating microelectrodes for neural interfaces realized by titanium micromachining
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{1454} | |||||||||||||||||||||||||||||||
Building High-level Features Using Large Scale Unsupervised Learning
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{1441} | |||||||||||||||||||||||||||||||
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
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{305} | |||||||||||||||||||||||||||||||
PMID-101388[0] Fine control of operantly conditioned firing patterns of cortical neurons.
____References____ | |||||||||||||||||||||||||||||||
{1450} |
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]
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PMID-25855189 Mapping Synapses by Conjugate Light-Electron Array Tomography
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{1448} | |||||||||||||||||||||||||||||||
PMID-15321069 Sparse coding of sensory inputs
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{1446} | |||||||||||||||||||||||||||||||
PMID-29074582 A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs
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{1442} | |||||||||||||||||||||||||||||||
PMID-30635577 Functional imaging of visual cortical layers and subplate in awake mice with optimized three photon microscopy
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{1440} | |||||||||||||||||||||||||||||||
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{1437} | |||||||||||||||||||||||||||||||
PMID-21280920 Optically sectioned in vivo imaging with speckle illumination HiLo microscopy
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{1436} |
ref: -0
tags: Airy light sheet microscopy attenuation compensation LSM imaging
date: 02-19-2019 04:51 gmt
revision:1
[0] [head]
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Light-sheet microscopy with attenuation-compensated propagation-invariant beams
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{1415} | |||||||||||||||||||||||||||||||
PMID-28777724 Active inference, curiosity and insight. Karl J. Friston, Marco Lin, Christopher D. Frith, Giovanni Pezzulo,
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{1423} | |||||||||||||||||||||||||||||||
PMID-27824044 Random synaptic feedback weights support error backpropagation for deep learning.
Our proof says that weights W0 and W evolve to equilibrium manifolds, but simulations (Fig. 4) and analytic results (Supple- mentary Proof 2) hint at something more specific: that when the weights begin near 0, feedback alignment encourages W to act like a local pseudoinverse of B around the error manifold. This fact is important because if B were exactly W + (the Moore- Penrose pseudoinverse of W ), then the network would be performing Gauss-Newton optimization (Supplementary Proof 3). We call this update rule for the hidden units pseudobackprop and denote it by ∆hPBP = W + e. Experiments with the linear net- work show that the angle, ∆hFA ]∆hPBP quickly becomes smaller than ∆hFA ]∆hBP (Fig. 4b, c; see Methods). In other words feedback alignment, despite its simplicity, displays elements of second-order learning. | |||||||||||||||||||||||||||||||
{1419} | |||||||||||||||||||||||||||||||
All-optical machine learning using diffractive deep neural networks
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{1430} | |||||||||||||||||||||||||||||||
PMID-28650477 Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy
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{1427} | |||||||||||||||||||||||||||||||
PMID-27934860 Scanning superlens microscopy for non-invasive large field-of-view visible light nanoscale imaging
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{1425} | |||||||||||||||||||||||||||||||
PMID-29375323 Fear learning regulates cortical sensory representation by suppressing habituation
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{1422} | |||||||||||||||||||||||||||||||
PMID-29205151 Towards deep learning with segregated dendrites https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716677/
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{1416} | |||||||||||||||||||||||||||||||
Learning data manifolds with a Cutting Plane method
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{1413} | |||||||||||||||||||||||||||||||
PMID-24711417 Evidence for a causal inverse model in an avian cortico-basal ganglia circuit
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{1412} |
ref: -0
tags: deeplabcut markerless tracking DCN transfer learning
date: 10-03-2018 23:56 gmt
revision:0
[head]
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Markerless tracking of user-defined features with deep learning
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{1411} | |||||||||||||||||||||||||||||||
PMID-20544831 The decade of the dendritic NMDA spike.
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{1410} | |||||||||||||||||||||||||||||||
Structure discovery in Nonparametric Regression through Compositional Kernel Search
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{1408} | |||||||||||||||||||||||||||||||
LDMNet: Low dimensional manifold regularized neural nets.
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{1404} | |||||||||||||||||||||||||||||||
PMID-25546652 Brain Tissue Responses to Neural Implants Impact Signal Sensitivity and Intervention Strategies
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{1384} | |||||||||||||||||||||||||||||||
PMID-28246640 Ultraflexible nanoelectronic probes form reliable, glial scar–free neural integration
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{1010} | |||||||||||||||||||||||||||||||
PMID-4708761 Design, Fabrication, and In Vivo Behavior of Chronic Recording Intracortical Microelectrodes
____References____ Salcman, Michael and Bak, Martin J. Design, Fabrication, and In Vivo Behavior of Chronic Recording Intracortical Microelectrodes Biomedical Engineering, IEEE Transactions on BME-20 4 253 -260 (1973) | |||||||||||||||||||||||||||||||
{1368} | |||||||||||||||||||||||||||||||
PMID-23451719 Synthetic Nanoelectronic Probes for Biological Cells and Tissue
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{1176} | |||||||||||||||||||||||||||||||
IEEE-6170092 (pdf) An ultra-compliant, scalable neural probe with molded biodissolvable delivery vehicle
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{1399} | |||||||||||||||||||||||||||||||
PMID-25128375 Chronic tissue response to carboxymethyl cellulose based dissolvable insertion needle for ultra-small neural probes.
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{1398} |
ref: -0
tags: platinum parylene electrodes brush dissolving stiffener gelatin
date: 12-28-2017 02:44 gmt
revision:0
[head]
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PMID-27159159 Embedded Ultrathin Cluster Electrodes for Long-Term Recordings in Deep Brain Centers.
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{1396} | |||||||||||||||||||||||||||||||
PMID-27791052 Ultrathin, transferred layers of thermally grown silicon dioxide as biofluid barriers for biointegrated flexible electronic systems
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{1392} | |||||||||||||||||||||||||||||||
PMID-29109247 Highly scalable multichannel mesh electronics for stable chronic brain electrophysiology
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{1389} |
ref: -0
tags: photoacoustic tomography mouse imaging q-switched laser
date: 05-11-2017 05:23 gmt
revision:1
[0] [head]
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{1390} |
ref: -0
tags: photoacoustic tomography mouse imaging q-switched laser
date: 05-11-2017 05:21 gmt
revision:0
[head]
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{1388} |
ref: -0
tags: PEDOT PSS electroplate eletrodeposition neural recording michigan probe stimulation CSC
date: 04-27-2017 01:36 gmt
revision:1
[0] [head]
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PMID-19543541 Poly(3,4-ethylenedioxythiophene) as a micro-neural interface material for electrostimulation
| |||||||||||||||||||||||||||||||
{1387} |
ref: -1977
tags: polyethylene surface treatment plasma electron irradiation mechanical testing saline seawater accelerated lifetime
date: 04-15-2017 06:06 gmt
revision:0
[head]
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Enhancement of resistance of polyethylene to seawater-promoted degradation by surface modification
| |||||||||||||||||||||||||||||||
{1385} | |||||||||||||||||||||||||||||||
Method of electropolishing tungsten wire US 3287238 A
| |||||||||||||||||||||||||||||||
{1250} |
ref: -0
tags: polyimide electrodes thermosonic bonding Stieglitz adhesion delamination
date: 03-06-2017 21:58 gmt
revision:7
[6] [5] [4] [3] [2] [1] [head]
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IEEE-6347149 (pdf) Improved polyimide thin-film electrodes for neural implants 2012
| |||||||||||||||||||||||||||||||
{747} | |||||||||||||||||||||||||||||||
PMID-17517431[0] Neural probe design for reduced tissue encapsulation in CNS.
____References____
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{1383} |
ref: -0
tags: carbon nanotube densification conductivity strength
date: 02-23-2017 02:52 gmt
revision:2
[1] [0] [head]
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Super-strong and highly conductive carbon nanotube ribbons from post-treatment methods
High-strength carbon nanotube fibre-like ribbon with high ductility and high electrical conductivity | |||||||||||||||||||||||||||||||
{1382} |
ref: -0
tags: iridium oxide nanotube intracellular recording electroplate MEA
date: 02-22-2017 22:41 gmt
revision:0
[head]
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PMID-24487777 Iridium oxide nanotube electrodes for sensitive and prolonged intracellular measurement of action potentials. | |||||||||||||||||||||||||||||||
{1380} |
ref: -0
tags: myoelectric EMG recording TMR prosthetics
date: 02-13-2017 20:43 gmt
revision:0
[head]
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PMID: Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation
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{1378} | |||||||||||||||||||||||||||||||
PMID-25803728 Neural stimulation and recording with bidirectional, soft carbon nanotube fiber microelectrodes.
PMID-23307737 Strong, light, multifunctional fibers of carbon nanotubes with ultrahigh conductivity.
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{1376} | |||||||||||||||||||||||||||||||
PMID-24677434 A Review of Organic and Inorganic Biomaterials for Neural Interfaces
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{1375} | |||||||||||||||||||||||||||||||
PMID-22905231 Neuronal recordings with solid-conductor intracellular nanoelectrodes (SCINEs).
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{1373} | |||||||||||||||||||||||||||||||
Contenders for high-modulus pitch-based carbon fiber: "
Tensile and Flextural Prperties of single carbon fibers
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{1372} |
ref: -0
tags: bone marrow transplant chimera immune response to indwelling electrode implant capadona inflammation
date: 02-02-2017 23:24 gmt
revision:1
[0] [head]
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PMID-24973296 The roles of blood-derived macrophages and resident microglia in the neuroinflammatory response to implanted intracortical microelectrodes.
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{1371} |
ref: -0
tags: nanotube tracking extracellular space fluorescent
date: 02-02-2017 22:13 gmt
revision:0
[head]
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PMID-27870840 Single-nanotube tracking reveals the nanoscale organization of the extracellular space in the live brain
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{1370} | |||||||||||||||||||||||||||||||
PMID-27256971 Multisite electrophysiological recordings by self-assembled loose-patch-like junctions between cultured hippocampal neurons and mushroom-shaped microelectrodes.
PMID-23380931 Multi-electrode array technologies for neuroscience and cardiology
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{1369} | |||||||||||||||||||||||||||||||
PMID-22231664 Vertical nanowire electrode arrays as a scalable platform for intracellular interfacing to neuronal circuits.
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{1366} |
ref: -0
tags: direct electrical stimulation neural mapping review
date: 01-26-2017 02:28 gmt
revision:0
[head]
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PMID-22127300 Direct electrical stimulation of human cortex -- the gold standard for mapping brain functions?
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{927} | |||||||||||||||||||||||||||||||
PMID-18672003[0] Neurotrophic electrode: method of assembly and implantation into human motor speech cortex.
____References____
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{1361} |
ref: -0
tags: neural coding rats binary permutation retrosplenial basolateral amygdala tetrode
date: 12-19-2016 07:39 gmt
revision:1
[0] [head]
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PMID-27895562 Brain Computation Is Organized via Power-of-Two-Based Permutation Logic.
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{1358} |
ref: -0
tags: china trustwothiness social engineering communism
date: 10-31-2016 05:42 gmt
revision:1
[0] [head]
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China 'social credit': Beijing sets up huge system So long as it purports to measure just one social variable -- 'trustworthiness' -- it might be a good idea. Many commerce websites (.. ebay ..) have these sort of rating systems already, and they are useful. When humans live in smaller communities something like this is in the shared consciousness. Peering into everyone's purchasing habits and hobbies, however, seems like it will be grossly myopic and, as the article says, Orwellian. Likely they will train a deep-belief network on past data of weakly and communist party defined success, with all purchasing and social media as the input data, and use that in the proprietary algorithm for giving people their scalars to optimize. This would be the ultimate party control tool -- a great new handle for controlling people's minds, even 'better' than capitalism. Surprising that the article only hints at this, and that the Chinese themselves seem rather clueless that it's a power play. In this sense, it's a very clever play to link it to reproduction. Other comments: These sorts of systems may be necessary in highly populated countries, where freedom and individuality are less valued and social cohesion is requisite. | |||||||||||||||||||||||||||||||
{1357} | |||||||||||||||||||||||||||||||
Physical Metallurgy of Refactory Metals and Alloys Properties of tungsten-rhenium alloys
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{1353} |
ref: -0
tags: PEDOT electropolymerization electroplating gold TFB borate counterion acetonitrile
date: 10-18-2016 07:49 gmt
revision:3
[2] [1] [0] [head]
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PMID-20715789 Investigation of near ohmic behavior for poly(3,4-ethylenedioxythiophene): a model consistent with systematic variations in polymerization conditions.
PMID-24576579 '''Improving the performance of poly(3,4-ethylenedioxythiophene) for brain–machine interface applications"
PEDOT-modified integrated microelectrodes for the detection of ascorbic acid, dopamine and uric acid
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{1354} |
ref: -0
tags: David Kleinfeld penetrating arterioles perfusion cortex vasculature
date: 10-17-2016 23:24 gmt
revision:1
[0] [head]
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PMID-17190804 Penetrating arterioles are a bottleneck in the perfusion of neocortex.
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{1270} |
ref: -0
tags: gold micrograin recording electrodes electroplating impedance
date: 10-17-2016 20:28 gmt
revision:5
[4] [3] [2] [1] [0] [head]
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PMID-23071004 Gold nanograin microelectrodes for neuroelectronic interfaces.
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{1352} | |||||||||||||||||||||||||||||||
PMID-27571550 Stable long-term chronic brain mapping at the single-neuron level.
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{1346} |
ref: -0
tags: super resolution imaging PALM STORM fluorescence
date: 09-21-2016 05:57 gmt
revision:0
[head]
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PMID-23900251 Parallel super-resolution imaging
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{1345} |
ref: -0
tags: nucleus accumbens caudate stimulation learning enhancement MIT
date: 09-20-2016 23:51 gmt
revision:1
[0] [head]
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{1341} |
ref: -0
tags: image registration optimization camera calibration sewing machine
date: 07-15-2016 05:04 gmt
revision:20
[19] [18] [17] [16] [15] [14] [head]
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Recently I was tasked with converting from image coordinates to real world coordinates from stereoscopic cameras mounted to the end-effector of a robot. The end goal was to let the user (me!) click on points in the image, and have the robot record that position & ultimately move to it. The overall strategy is to get a set of points in both image and RW coordinates, then fit some sort of model to the measured data. I began by printing out a grid of (hopefully evenly-spaced and perpendicular) lines via a laserprinter; spacing was ~1.1 mm. This grid was manually aligned to the axes of robot motion by moving the robot along one axis & checking that the lines did not jog. The images were modeled as a grating with quadratic phase in texture coordinates: (1) (2) (3) The 1000 was used to make the parameter search distribution more spherical; were bias terms to seed the solver; 0.97 was a duty-cycle term fit by inspection to the image data; (3) is a modified sigmoid. was then optimized over the parameters using a GPU-accelerated (CUDA) nonlinear stochastic optimization: (4) Optimization was carried out by drawing parameters from a normal distribution with a diagonal covariance matrix, set by inspection, and mean iteratively set to the best solution; horizontal and vertical optimization steps were separable and carried out independently. The equation (4) was sampled 18k times, and equation (3) 34 billion times per frame. Hence the need for GPU acceleration. This yielded a set of 10 parameters (again, and were bias terms and kept constant) which modeled the data (e.g. grid lines) for each of the two cameras. This process was repeated every 0.1 mm from 0 - 20 mm height (z) from the target grid, resulting in a sampled function for each of the parameters, e.g. . This required 13 trillion evaluations of equation (3). Now, the task was to use this model to generate the forward and reverse transform from image to world coordinates; I approached this by generating a data set of the grid intersections in both image and world coordinates. To start this process, the known image origin was used to find the corresponding roots of the periodic axillary functions : (5) (6) Or .. (7) (8) From this, we get variables which are the offsets to align the sine functions with the physical origin. Now, the reverse (world to image) transform was needed, for which a two-stage newton scheme was used to solve equations (7) and (8) for . Note that this is an equation of phase, not image intensity -- otherwise this direct method would not work! First, the equations were linearized with three steps of (9-11) to get in the right ballpark:
(9) (10) and (11) where mldivide is the Matlab operator. Then three steps with the full Jacobian were made to attain accuracy: (12) (13) (14) Solutions were verified by plugging back into equations (7) and (8) & verifying were the same. Inconsistent solutions were discarded; solutions outside the image space were also discarded. The process (10) - (14) was repeated to tile the image space with gird intersections, as indicated in (9), and this was repeated for all in , resulting in a large (74k points) dataset of , which was converted to full real-world coordinates based on the measured spacing of the grid lines, . Between individual z steps, was re-estimated to minimize (for a current ): (15) with grid-search, and the method of equations (9-14). This was required as the stochastic method used to find original image model parameters was agnostic to phase, and so phase (via parameter ) could jump between individual measurements (the origin did not move much between successive measurements, hence (15) fixed the jumps.) To this dataset, a model was fit: (16) Where , , , and . was introduced as an axillary variable to assist in perspective mapping, ala computer graphics. Likewise, were scaled so the quadratic nonlinearity better matched the data. The model (16) was fit using regular linear regression over all rows of the validated dataset. This resulted in a second set of coefficients for a model of world coordinates to image coordinates; again, the model was inverted using Newton's method (Jacobian omitted here!). These coefficients, one set per camera, were then integrated into the C++ program for displaying video, and the inverse mapping (using closed-form matrix inversion) was used to convert mouse clicks to real-world coordinates for robot motor control. Even with the relatively poor wide-FOV cameras employed, the method is accurate to , and precise to . | |||||||||||||||||||||||||||||||
{1338} | |||||||||||||||||||||||||||||||
ZeroMQ -- much better sockets framework than native TCP/UDP sockets.
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{1336} | |||||||||||||||||||||||||||||||
A contact lens with embedded sensor for monitoring tear glucose level
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META II: Digital Vellum in the Digital Scriptorium: Revisiting Schorre's 1962 compiler-compiler
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{1326} | |||||||||||||||||||||||||||||||
PMID-25627426 Rapid evaluation of the durability of cortical neural implants using accelerated aging with reactive oxygen species.
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{1325} | |||||||||||||||||||||||||||||||
https://mitpress.mit.edu/sites/default/files/titles/free_download/9780262526548_Art_of_Insight.pdf | |||||||||||||||||||||||||||||||
{1306} | |||||||||||||||||||||||||||||||
PMID-18640155 Characterization of flexible ECoG electrode arrays for chronic recording in awake rats.
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{1313} | |||||||||||||||||||||||||||||||
Neural Stimulation and Recording with Bidirectional, Soft Carbon Nanotube Fiber Microelectrodes
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{1304} | |||||||||||||||||||||||||||||||
PMID-21379404 Creating low-impedance tetrodes by electroplating with additives
Conclusion: 75% PEG, commercial electropating solution, 0.1ua current pluses to 250K or less.
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{1299} |
ref: -0
tags: wirebonding finishes gold nickel palladium electroless electrolytic
date: 09-21-2014 02:53 gmt
revision:3
[2] [1] [0] [head]
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Why palladium? To prevent black nickel: http://tayloredge.com/reference/Electronics/PWB/BlackPad_ITRI_Round1.PD Introduction The use of electroless nickel / immersion gold (E.Ni/I.Au) as a circuit board finish has grown significantly in the last few years. It provides a flat board finish, is very solderable, provides a precious metal contact surface and the nickel strengthens the plated holes. However, as the usage of E.Ni/I.Au increased, a problem was found on BGA (Ball Grid Array) components. An open or fractured solder joint sometimes appears after board assembly on the occasional BGA pad. The solder had wet and dissolved the gold and formed a weak intermetallic bond to the nickel. This weak bond to the nickel readily fractures under stress or shock, leaving an open circuit. The incidence of this problem appears to be very sporadic and a low ppm level problem, but it is very unpredictable. A BGA solder joint cannot be touched-up without the component being removed. After the BGA component is removed, a black pad is observed at the affected pad site. This black pad is not readily solderable, but it can be repaired. From: http://www.smtnet.com/Forums/index.cfm?fuseaction=view_thread&Thread_ID=4430 You don't have enough gold. Your 2uin is too porous and is allowing the nickel to corrode. Prove that this by hand soldering to these pads with a more active flux, like a water soluble solder paste, than you are using. You must have at least 3uin of immersion gold. Seriously consider >5uin. Your nickel thickness is fine. Although if you wanted to trade costs, consider giving-up nickel to 150uin thickness, while increasing the gold thickness. Gold over electroless nickel creates brittle joints because of phosphorous in the nickel plating bath. The phosphorous migrates into the over-plating. Electrolytic nickel and gold plating should not be a problem. If you stay with the electroless nickel, keep the phosphorous at a mid [7 - 9%] level. Just as important, don't let the immersion gold get too aggressive. The immersion gold works by corroding the nickel. If it is too aggressive it takes away the nickel and leave phosphorous behind. This makes it look like the phosphorous level is too high in the nickel bath. Gold purity is very important for any type of wire bonding process. For aluminum wedge bonding, gold should have a purity of 99. 99% [no thalium] and the nickel becomes critical. No contaminates and the nickel wants to be plated a soft as possible. This requires good control of Ph and plating chemicals in the nickel-plating bath. Harman "Wire Bonding In Microelectronics" McGraw-Hill is a good resource for troubleshooting wire bonding. I reviewed it in the SMTnet Newsletter a couple of months ago. That said, electrolytic nickel + electrolytic gold does work well -- perhaps even better than ENEPIG: | |||||||||||||||||||||||||||||||
{1296} |
ref: -0
tags: physical principles of scalable neural recording marblestone
date: 08-25-2014 20:21 gmt
revision:0
[head]
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PMID-24187539 Physical principles for scalable neural recording.
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{1291} | |||||||||||||||||||||||||||||||
Weldability of Tungsten and Its Alloys
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{1112} | |||||||||||||||||||||||||||||||
PMID-21301965[0] Novel multi-sided, microelectrode arrays for implantable neural applications.
____References____
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{1290} | |||||||||||||||||||||||||||||||
PMID-270699 Local control of neurite development by nerve growth factor.
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{1289} | |||||||||||||||||||||||||||||||
images/1289_1.pdf -- Debugging reinvented: Asking and Answering Why and Why not Questions about Program Behavior.
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{1288} |
ref: -0
tags: automatic programming inductive functional igor
date: 07-29-2014 02:07 gmt
revision:0
[head]
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Inductive Rule Learning on the Knowledge Level.
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{1285} | |||||||||||||||||||||||||||||||
various:
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{1279} | |||||||||||||||||||||||||||||||
PMID-23024377 Plasma-assisted atomic layer deposition of Al(2)O(3) and parylene C bi-layer encapsulation for chronic implantable electronics.
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PMID-23860226 A carbon-fiber electrode array for long-term neural recording.
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{1272} | |||||||||||||||||||||||||||||||
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{1258} |
ref: -0
tags: polyimide platinum electrodes Spain longitudinal intrafasicular adhesion delamination
date: 10-05-2013 22:24 gmt
revision:4
[3] [2] [1] [0] [head]
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PMID-17278585 Assessment of biocompatibility of chronically implanted polyimide and platinum intrafascicular electrodes. 2007
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{1257} |
ref: -0
tags: Anna Roe optogenetics artificial dura monkeys intrinisic imaging
date: 09-30-2013 19:08 gmt
revision:3
[2] [1] [0] [head]
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PMID-23761700 Optogenetics through windows on the brain in nonhuman primates
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{1226} | |||||||||||||||||||||||||||||||
PMID-23142839 Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces.
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{1249} | |||||||||||||||||||||||||||||||
PMID-21273316 Physiological clustering of visual channels in the mouse retina
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{1242} | |||||||||||||||||||||||||||||||
We're making parylene electrodes for neural recording, and one critical step is connecting them to recording electronics. Currently Berkeley uses ACF (anisotropic conductive film) for connection, which is widely used for connecting flex tape to LCD panels, or for connecting driver chips to LCD glass. According to the internet, pitches can be as low as 20um, with pad areas as low as 800um^2. source However, this does not seem to be a very reliable nor compact process with platinum films on parylene, possibly because ACF bonding relies on raised areas between mated conductors (current design has the Pt recessed into the parylene), and on rigid substrates. ACF consists of springy polymer balls coated in Ni and Au and embedded in a thermoset epoxy resin. The ACF film is put under moderate temperature (180C) and pressure (3mpa, 430psi), which causes the epoxy to cure in a state that leaves the gold/nickel/polymer balls to be compressed between the two conductors. Hence, even if the conductors move slightly due to thermal cycling, the small balls maintain good mechanical and electrical contact. The balls are dispersed sufficiently in the epoxy matrix that there is little to no chance of conduction between adjacent pads. (Or so I have learned from the internet.) Now, as mentioned, this is an imperfect method for joining Pt on parylene films, possibly because the parylene is so flexible, and the platinum foil is very thin (200-300 nm). Indeed, platinum does not bond very strongly to parylene, hence care must be taken to allow sufficient overlap to prevent water ingress. My proposed solution -- to be tested shortly -- is to use a low-melting temperature metal with strong wetting ability -- such as Field's metal (bismuth, tin, indium, melting point 149F, see http://www.gizmology.net/fusiblemetals.htm) to low-temperature solder the platinum to a carrier board (initially) or to a custom amplifier ASIC (later!). Parylene is stable to 200C (392F), so this should be safe. One worry is that the indium/bismuth will wet the parylene or polyimide, too; however I consider this unlikely due to the difficulty in attaching parylene to any metal. That said, there must be good reason why ACF is so popular, so perhaps a better ultimate solution is to stiffen the parylene (or ultimately polyimide) substrate so that it can support both the temperature/pressure of ACF bonding and the stress of a continued electrical/mechanical bond to polyimide fan-out board or ASIC. It may also be possible to gold or nickel electroplate the connector pads to be slightly raised instead of recessed. Update: ACF bond to rigid 1/2 oz copper, 4mil trace / space connector (3mil trace/space board): Note that the copper traces are raised, and the parylene is stretched over the uneven surface (this is much easier to see with the stereo microscope). To the left of the image, the ACF paste has been sqeezed out from between the FR4 and parylene. Also note that the platinum can make potential contact with vias in the PCB. Update 7/2: Fields metal (mentioned above) does stick to platinum reasonably well, but it also sticks to parylene (somewhat), and glass (exceptionally well!). In fact, I had a difficult time removing traces of field's metal from the Pyrex beakers that I was melting the metal with. These beakers were filled with boiling water, which may have been the problem. When I added flux (Kester flux-pen 951 No-clean MSDS), the metal became noticeably more shiny, and the contact angle increased on the borosilicate glass (e.g. looked more like mercury); this leads me to believe that it is not the metal itself that attaches to glass, but rather oxides of indium and bismuth. Kester 951 flux consists of:
After coating the parylene/platinum sample with flux, I raised the field's metal to the flux activation point, which released some smoke and left brown organic residues on the bottom of the glass dish. Then I dipped the parylene probe into the molten metal, causing the flux again to be activated, and partially wetting the platinum contacts. The figure below shows the result: Note the incomplete wetting, all the white solids left from the process, and how the field's metal caused the platinum to delaminate from the parylene when the cable was (accidentally) flexed. Tests with platinum foil revealed that the metal bond was not actually that strong, significantly weaker than that made with a flux-core SnPb solder. also, I'm not sure of the activation temperature of this flux, and think I may have overheated the parylene. Update 7/10: Am considering electrodeless Ni / Pt / Au deposition, which occurs in aqueous solution, hence at much lower temperatures than e-beam evaporation Electrodeless Ni ref. On polyimide substrates, there is extensive literature describing how to activate the surface for plating: Polyimides and Other High Temperature Polymers: Synthesis ..., Volume 4. Parylene would likely need a different possibly more aggressive treatment, as it does not have imide bonds to open. Furthermore, if the parylene / polyimide surface is *not* activated, the electrodeless plating could be specific to the exposed electrode and contact sites, which could help to solve the connector issue by strengthening & thickening the contact areas. The second fairly obvious solution is to planarize the contact site on the PCB, too, as seen above. ACF bonds can be quite reliable; last night I took apart (and successfully re-assembled) my 32" Samsung LCD monitor, and none of the flex-on-glass or chip-on-flex binds failed (despite my clumsy hands!). | |||||||||||||||||||||||||||||||
{1247} | |||||||||||||||||||||||||||||||
Characterization of parylene-C film as an encapsulation material for neural interface devices
___Low Dielectric Constant Materials for Ic Applications___ edited by Paul Shin Ho, Jihperng Leu, Wei William Lee
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{1246} |
ref: -0
tags: parylene microchannel micromolding glass transition temperature microfluidics
date: 06-28-2013 17:34 gmt
revision:3
[2] [1] [0] [head]
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Parylene micromolding, a rapid low-cost fabrication method for parylene microchannel
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{1245} |
ref: -0
tags: polyimide aging deadhesion humidity water absorption
date: 06-28-2013 02:07 gmt
revision:1
[0] [head]
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Environmental Aging and Deadhesion of Polyimide Dielectric films
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{1243} | |||||||||||||||||||||||||||||||
IEEE-5734597 (pdf) A novel platinum nanowire-coated neural electrode and its electrochemical and biological characterization
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{1193} | |||||||||||||||||||||||||||||||
PMID-23010756[0] Comprehensive characterization and failure modes of tungsten microwire arrays in chronic neural implants.
____References____
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{1241} | |||||||||||||||||||||||||||||||
http://thesis.library.caltech.edu/4671/1/PhDThesisFinalChanglinPang.pdf
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{1238} | |||||||||||||||||||||||||||||||
PMID-23428842 Chronic intracortical microelectrode arrays induce non-uniform, depth-related tissue responses.
This result is supported by previous papers:
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{1232} | |||||||||||||||||||||||||||||||
PMID-22726828 The Brain Activity Map Project and the Challenge of Functional Connectomics
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PMID-23514423 Nanotools for Neuroscience and Brain Activity Mapping
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{597} | |||||||||||||||||||||||||||||||
PMID-16425835Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex
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{999} | |||||||||||||||||||||||||||||||
IEEE-4065599 (pdf) Comments on Microelectrodes
____References____ ' ''' () | |||||||||||||||||||||||||||||||
{781} | |||||||||||||||||||||||||||||||
PMID-16198003[0] Response of brain tissue to chronically implanted neural electrodes
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{946} | |||||||||||||||||||||||||||||||
PMID-1256090[0] A new chronic recording intracortical microelectrode
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{748} | |||||||||||||||||||||||||||||||
PMID-18485471[0] Characterization of microglial attachment and cytokine release on biomaterials of differing surface chemistry
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{1221} | |||||||||||||||||||||||||||||||
PMID-21775782[0] Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex (Shenoy)
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{1036} | |||||||||||||||||||||||||||||||
Things to read! decoding:
electrodes:
other random scribblings: Vascularization {1027} histology {736},{737} and size {1028},{747},{1026}, insulation {1033}. How very very important -- as important or moreso than the recording technology. What has happened to {149} ? | |||||||||||||||||||||||||||||||
{1220} | |||||||||||||||||||||||||||||||
PMID-20577634 Biocompatibility of intracortical microelectrodes: current status and future prospects.
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{1177} | |||||||||||||||||||||||||||||||
IEEE-1196780 (pdf) 3D flexible multichannel neural probe array
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{895} | |||||||||||||||||||||||||||||||
IEEE-1605268 (pdf) Evaluation of the Stability of Intracortical Microelectrode Arrays
____References____ Xindong Liu and McCreery, D.B. and Bullara, L.A. and Agnew, W.F. Evaluation of the stability of intracortical microelectrode arrays Neural Systems and Rehabilitation Engineering, IEEE Transactions on 14 1 91 -100 (2006) | |||||||||||||||||||||||||||||||
{1114} | |||||||||||||||||||||||||||||||
PMID-22170970[0] A system for recording neural activity chronically and simultaneously from multiple cortical and subcortical regions in non-human primates. ____References____
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{898} | |||||||||||||||||||||||||||||||
PMID-19486899[0] Toward a comparison of microelectrodes for acute and chronic recordings.
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{1214} | |||||||||||||||||||||||||||||||
PMID-7972766 Brain and cerebrospinal fluid motion: real-time quantification with M-mode MR imaging.
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{1213} | |||||||||||||||||||||||||||||||
PMID-20153370[0] A bio-friendly and economical technique for chronic implantation of multiple microelectrode arrays ____References____
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{78} | |||||||||||||||||||||||||||||||
PMID-17067683[0] A floating metal microelectrode array for chronic implantation
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{311} | |||||||||||||||||||||||||||||||
PMID-9350963 A floating microwire technique for multichannel neural recording and stimulation in the awake rat
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{1105} |
ref: Bullara-1983.09
tags: electrode grinding insulation stimulation
date: 01-28-2013 00:27 gmt
revision:1
[0] [head]
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PMID-6632958[0] A microelectrode for delivery of defined charge densities.
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{736} | |||||||||||||||||||||||||||||||
PMID-10498377[0] Stability of the interface between neural tissue and chronically implanted intracortical microelectrodes.
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{1195} | |||||||||||||||||||||||||||||||
PMID-21270781[0] How advances in neural recording affect data analysis.
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{746} | |||||||||||||||||||||||||||||||
PMID-10906696[0] Tissue response to single-polymer fibers of varying diameters: evaluation of fibrous encapsulation and macrophage density.
"
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{1211} | |||||||||||||||||||||||||||||||
PMID-9723616[0] Signal-dependent noise determines motor planning.
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{897} | |||||||||||||||||||||||||||||||
PMID-21654037[0] In vivo deployment of mechanically adaptive nanocomposites for intracortical microelectrodes
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{1027} | |||||||||||||||||||||||||||||||
PMID-16425835[0] Reliability of signals from a chronically implanted, silicon-based electrode array
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{1198} | |||||||||||||||||||||||||||||||
PMID-22049097[0] Mechanically adaptive intracortical implants improve the proximity of neuronal cell bodies.
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{1208} |
ref: Lewitus-2011.08
tags: dissolving polymer electrodes histology degrading
date: 01-25-2013 01:31 gmt
revision:2
[1] [0] [head]
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PMID-21609850[0] The fate of ultrafast degrading polymeric implants in the brain.
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{1205} | |||||||||||||||||||||||||||||||
PMID-15698656[0] A comparison of chronic multi-channel cortical implantation techniques: manual versus mechanical insertion.
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{1207} |
ref: -0
tags: Shenoy eye position BMI performance monitoring
date: 01-25-2013 00:41 gmt
revision:1
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PMID-18303802 Cortical neural prosthesis performance improves when eye position is monitored.
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{1206} | |||||||||||||||||||||||||||||||
PMID-19164034 Cortical recording with polypyrrole microwire electrodes.
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PMID-17409479[0] Thin microelectrodes reduce GFAP expression in the implant site in rodent somatosensory cortex.
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{749} | |||||||||||||||||||||||||||||||
PMID-17266019[0] The brain tissue response to implanted silicon microelectrode arrays is increased when the device is tethered to the skull. ____References____
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PMID-21499255[0] Reversible large-scale modification of cortical networks during neuroprosthetic control.
Other notes:
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{1052} | |||||||||||||||||||||||||||||||
IEEE-5332822 (pdf) Neural prosthetic systems: Current problems and future directions
____References____ 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) | |||||||||||||||||||||||||||||||
{1192} |
ref: -2002
tags: sea slugs flexible electrodes polymide Washington
date: 01-04-2013 18:46 gmt
revision:0
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IEEE-1002325 (pdf) Silicon micro-needles with flexible interconnections
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{1040} | |||||||||||||||||||||||||||||||
PMID-22022568[0] Multiplexed, High Density Electrophysiology with Nanofabricated Neural Probes
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{1188} | |||||||||||||||||||||||||||||||
IEEE-906517 (pdf) Flexible microelectrode arrays with integrated insertion devices
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{1178} | |||||||||||||||||||||||||||||||
PMID-23160191 Novel flexible Parylene neural probe with 3D sheath structure for enhancing tissue integration
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{1187} |
ref: -0
tags: neural recording topologies circuits operational transconductance amplifiers
date: 01-02-2013 20:00 gmt
revision:0
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PMID-22163863 Recent advances in neural recording microsystems. | |||||||||||||||||||||||||||||||
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PMID-22308458 Optically monitoring voltage in neurons by photo-induced electron transfer through molecular wires.
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PMID-22574249 High spatial and temporal resolution wide-field imaging of neuron activity using quantum NV-diamond.
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PMID-16050036 Imaging brain activity with voltage- and calcium-sensitive dyes.
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http://www.redshirtimaging.com/redshirt_neuro/neuro_lib_2.htm
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PMID-20844600 Detection of Neural Action Potentials Using Optical Coherence Tomography: Intensity and Phase Measurements with and without Dyes.
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PMID-19654752 Detecting intrinsic scattering changes correlated to neuron action potentials using optical coherence imaging.
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{1173} | |||||||||||||||||||||||||||||||
List of links from Moshe Looks google tech talk:
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{1172} | |||||||||||||||||||||||||||||||
Recently decided to move myopen's sorting program from sqlite-based persistent state to matlab persistent state, for better interoperability with lab rigs & easier introspection. For this I wrote a class for serializing STL / boost based one, two, and three dimensional resizeable containers to and from matlab files. The code has been tested (albeit not extensively), and therefore may be of use to someone else, even if as an example. See: http://code.google.com/p/myopen/source/browse/trunk/common_host/matStor.cpp As you can see from the header (below), the interface is nice and concise! #ifndef __MATSTOR_H__ #define __MATSTOR_H__ class MatStor{ typedef boost::multi_array<float, 3> array3; typedef boost::multi_array<float, 2> array2; std::string m_name; //name of the file. std::map<std::string, std::vector<float> > m_dat1; //one dimensional std::map<std::string, array2> m_dat2; //two dimensions std::map<std::string, array3> m_dat3; //three! public: MatStor(const char* fname); ~MatStor(); void save(); void setValue(int ch, const char* name, float val); void setValue2(int ch, int un, const char* name, float val); void setValue3(int ch, int un, const char* name, float* val, int siz); float getValue(int ch, const char* name, float def); float getValue2(int ch, int un, const char* name, float def); void getValue3(int ch, int un, const char* name, float* val, int siz); }; #endif | |||||||||||||||||||||||||||||||
{1171} | |||||||||||||||||||||||||||||||
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{1169} |
ref: -0
tags: artificial intelligence projection episodic memory reinforcement learning
date: 08-15-2012 19:16 gmt
revision:0
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Projective simulation for artificial intelligence
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{1155} | |||||||||||||||||||||||||||||||
A frequent task in the lab is to sort spikes (extracellular neural action potentials) from background noise. In the lab we are working on doing this wirelessly; to minimize power consumption, spike sorting is done before the radio. In this way only times of spikes need be transmitted, saving bandwidth and power. (This necessitates a bidirectional radio protocol, but this is a worthy sacrifice). In most sorting programs (e.g. Plexon), the raw signal is first thresholded, then waveform snippets (typically 32 samples long) are compared to a template to accept/reject them, or to sort them into different units. The comparison metric is usually the mean-squared error, MSE, aka the L2 norm. This makes sense, as the spike shapes are assumed to be stereotyped (they may very well not be), and the noise white / uncorrelated (another debatable assumption). On the headstage we are working with for wireless neural recording, jumps and memory moves are expensive operations, hence we've elected to do no waveform extraction, and instead match continuously match. By using the built-in MPEG compression opcodes, we can compute the L1 norm at a rate of 4 samples / clock -- very efficient. However, this was more motivated by hardware considerations an not actual spike sorting practice. Literature suggests that for isolating a fixed-pattern signal embedded in noise, the best solution is instead a matched filter. Hence, a careful study of spike-sorting was attempted in matlab, given the following assumptions: fixed spike shape (this was extracted from real data), and uncorrelated band-limited noise. The later was just white noise passed through a bandpass filter, e.g. cheby1(3, 2, [500/15e3 7.5/15]) Where the passband edges are 500 Hz and 15kHz, at a sampling rate of 30kHz. (Actual rate is 31.25kHz). Since the spike times are known, we can rigorously compare the Receiver Operating Characteristic (ROC) and the area under curve (AUC) for different sorting algorithms. Four were tried: L1 (as mentioned above, motivated by the MPEG opcodes), L2 (Plexon), FIR matched filter, and IIR matched filter. The latter was very much an experiment -- IIR filters are efficiently implemented on the blackfin processor, and they generally require fewer taps than their equivalent FIR implementation. To find an IIR equivalent to a given FIR matched filter (whose impulse response closely looks like the actual waveshape, just time-reversed), the filter parameters were simply optimized to match the two impulse responses. To facilitate the search, the denominator was specified in terms of complex conjugate pole locations (thereby constraining the form of the filter), while the numerator coefficients were individually optimized. Note that this is not optimizing given the objective to maximize sorting quality -- rather, it is to make the IIR filter impulse response as close as possible to the FIR matched filter, hence computationally light. And yet: the IIR filter outperforms the FIR matched filter, even though the IIR filter has 1/3 the coefficients (10 vs 32)! Below is the AUC quality metric for the four methods. And here are representative ROC curves at varying spike SNR ratios. The remarkable thing is that even at very low SNR, the matched IIR filter can reliably sort cells from noise. (Note that the acceptable false positive here should be weighted more highly; in the present analysis true positive and false positive are weighted equally, which is decidedly non-Bayesian given most of the time there is no spike.) The matched IIR filter is far superior to the normal MSE to template / L2 norm method -- seems we've been doing it wrong all along? As for reliably finding spikes / templates / filters when the SNR < 0, the tests above - which assume an equal number of spike samples and non-spike samples -- are highly biased; spikes are not normally sortable when the SNR < 0. Upon looking at the code again, I realized three important things:
Including #1 above, as expected, dramatically increased the false positive rate, which is to be expected and how the filters will be used in the real world. #2 did not dramatically impact any of the discriminators, which is good. #3 alleviated the gap between the IIR and FIR filters, and indeed the FIR matched filter performance now slightly exceeds the IIR matched filer. Below, AUC metric for 4 methods. And corresponding ROC for 6 different SNR ratios (note the SNRs sampled are slightly different, due to the higher false positive rate). One thing to note: as implemented, the IIR filter requires careful matching of poles and zeros, and is may not work with 1.15 fixed-point math on the Blackfin. The method really deserves to be tested in vivo, which I shall do shortly. More updates: See www.aicit.org/jcit/ppl/JCIT0509_05.pdf -- they add an 'adjustment' function to the matched filter due to variance in the amplitude of spikes, which adds a little performance at low SNRs.
Sigma is the standard deviation of x(t), n and k determine 'zoom intensity and zoom center'. The paper is not particularly well written - there are some typos, and their idea seems unjustified. Still the references are interesting:
Update: It is not to difficult to convert FIR filters to IIR filters using simple numerical optimization. Within my client program, this is done using simulated annealing; have tested this using fminsearch in matlab. To investigate the IIR-filter fitting problem more fully, I sliced the 10-dimensional optimization space along pairs of dimensions about the optimum point as found using fminsearch. The parameters are as follows:
The figure below plots the +-1 beyond the optimum for each axis pair. Click for full resolution image. Note that the last parameter is discrete, hence steps in the objective function. Also note that the problem is perfectly quadratic for the numerator, as expected, which is why LMS works so well. Note that for the denominator pole locations, the volume of the optimum is small, and there are interesting features beyond this. Some spaces have multiple optima. The next figure plots +-0.1 beyond the optimum for each axis vs. every other one. It shows that, at least on a small scale, the problem becomes very quadratic in all axes hence amenable to line or conjugate gradient search. Moving away from planes that pass through a found optima, what does the space look like? E.g. From a naive start, how hard is it to find at least one workable solution? To test this, I perturbed the found optimum with white noise in the parameters std 0.2, and plotted the objective function as before, albeit at higher resolution (600 x 600 points for each slice). These figures show that there can be several optima in the denominator, but again it appears that a very rough exploration followed by gradient descent should arrive at an optima. | |||||||||||||||||||||||||||||||
{253} | |||||||||||||||||||||||||||||||
PMID-14634657[0]Inference of hand movements from local field potentials in monkey motor cortex
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PMID-22157115 Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials.
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http://www.mdpi.com/1424-8220/8/10/6704/pdf NeuroMEMS: Neuro Probe Microtechnologies
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PMID-10223510 Chronic recording capability of the Utah Intracortical Electrode Array in cat sensory cortex.
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Just to my left, a woman in a walker rolled into the library, and promptly complained to the police officer on duty about chest pains. The library faces a square in the middle of Springfield where teenagers, shirtless hippies, skateboarders, and other non-mainstream people kill time in the warm afternoon. The library as such is a cool haven to read and access the internet -- several teenagers were playing WoW on the library computers, and I too am tapping into the resource. A possibly adrift artsy-type man of about my age similarly came to conduct his wayward business, having 'just ended up in Springfield', saying it as both and excuse and a badge of pride evincing his free spirit. The woman is one of the classic types of hypochondriac, and though I'm only listening to them the EMT and police men know this, but they also know that on the off chance of being wrong, not taking the situation seriously could be a disaster. And so they administered simple blood pressure and pulse rate tests, both which seemed normal, then went about convincing her that she needed to be taken to the hospital to be completely checked out, thereby shifting the burden of liability to a place better protected by the standard operating procedure of a battery of tests. The woman immediately protested, worried about the heavy cost of a ambulance ride, coupled with a paranoia that she would lose her walker. To this the EMT -- a short woman with her practical ponytail shoved through a baseball cap, as often they do -- let glints of irritation show through, asking her repeatedly to decide which hospital she wished to go to, and then asking her to arrange another means to the hospital. The woman protested, but the EMT could scarecly be blamed -- she is stuck in a system not of her design -- and somehow the smooth-souled librarian, who before had been placating library customers by putting holds on books, convinced both parties to go to the nearest hopsital. How exactly this was done I'll forever remain in ignorance, for another ambulance spun through the downtown circle at that instant, scattering sports cars, stopping sedans, and causing the skaters to pause their idling and look. The incident vaguely reminds me when I had similar issues in Brooklyn, when i was sufficiently pained to drive my ass through the dirty orange-lit streets to a hospital in Williamsburg. They proceeded to do drug tests on me, despite my insistences, but everything checked out fine. In retrospect, the pain was likely heartburn antagonized by psychological isolation; this was before I really learned to listen to myself, and take care of the social and more basic physiological needs. The walker woman fell through these same cracks in a likely preventable but now very expensive way. Meanwhile, a large black transsexual and a wrinkly white guy walk hurriedly past the plate glass windows of the library, talking animatedly. They may be in a fissure of sorts, but i doubt they consider it a fall... | |||||||||||||||||||||||||||||||
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I highly agree with this philosophy / this deconstruction of the flow of information in human structures: http://www.lostgarden.com/2012/04/loops-and-arcs.html On criticism as a meta-arc game: "In the past I've discussed criticism as a game that attempts to revisit an arc repeatedly and embellish it with additional meaning. The game is to generate essays superficially based on some piece of existing art. In turn, other players generate additional essays based off the first essays. This acts as both a referee mechanism and judge. Score is accumulated via reference counts and by rising through an organization hierarchy. It is a deliciously political game of wit that is both impenetrable to outsiders and nearly independent of the actual source arcs. Here creating an arc becomes a move in the larger game. " | |||||||||||||||||||||||||||||||
{1158} | |||||||||||||||||||||||||||||||
This morning hundreds of bees were swarming outside my front door -- a fact is not without reason, as my roommate makes honey, and her hive just today outgrew the apiary. Hence the hive split this morning, and one queen be left to wait on a branch outside while scouts searched for good places to build a new colony; meanwhile hundreds of non-scout workers were swarming around her. Bees are amazing. Anyway, a friend sent a link to an article describing how to generate microwatts of energy off a flying insect, which led me to wonder how much energy those bees could have been producing instead of milling protectively about their queen.
Conclusion: let them make honey :-) (1) Half the bees visible were resting on leaves, not madly flapping. (2) Rough wiki-google estimate. | |||||||||||||||||||||||||||||||
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PMID-22448159 Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational Bayes.
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PMID-15496658 Neuronal oscillations in the basal ganglia and movement disorders: evidence from whole animal and human recordings.
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{1151} | |||||||||||||||||||||||||||||||
IEEE-4353634 (pdf) Optimal Operating Frequency in Wireless Power Transmission for Implantable Devices
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{696} |
ref: Jarosiewicz-2008.12
tags: Schwartz BMI learning perturbation
date: 03-07-2012 17:11 gmt
revision:2
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PMID-19047633[0] Functional network reorganization during learning in a brain-computer interface paradigm.
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{166} | |||||||||||||||||||||||||||||||
PMID-19744484 What can man do without basal ganglia motor output? The effect of combined unilateral subthalamotomy and pallidotomy in a patient with Parkinson's disease.
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PMID-16271465 The basal ganglia: learning new tricks and loving it
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{1018} |
ref: Rouse-2011.06
tags: BMI chronic DBS bidirectional stimulator Washington Medtronic ASIC translational
date: 03-05-2012 23:56 gmt
revision:3
[2] [1] [0] [head]
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PMID-21543839[0] A chronic generalized bi-directional brain-machine interface.
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{1117} | |||||||||||||||||||||||||||||||
PMID-9004351[0] The basal ganglia: focused selection and inhibition of competing motor programs.
Electrophysiology:
STN:
GPi:
SNr:
GPe:
SNpc:
Synthesis:
Parkinson's disease:
Huntingtons
STN / Hemiballismus
Experimental lesions:
Large papers are not dissimilar from large software projects -- they take time, iteration, and concentration. Papers, however, are harder as the feedback is not immediate and gratifying. ____References____
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{1137} | |||||||||||||||||||||||||||||||
PMID-6389041 Functional organization of the basal ganglia: contributions of single-cell recording studies.
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{1150} |
ref: -0
tags: Albin basal ganglia dopamine 1989 parkinsons huntingtons hemiballismus
date: 03-02-2012 00:28 gmt
revision:1
[0] [head]
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PMID-2479133 The functional anatomy of basal ganglia disorders.
Saccades:
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PMID-16153803[0] The robot basal ganglia: action selection by an embedded model of the basal ganglia
My thoughts:
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{1079} | |||||||||||||||||||||||||||||||
PMID-21147836[0] Resting oscillatory cortico-subthalamic connectivity in patients with Parkinson’s disease
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{1148} | |||||||||||||||||||||||||||||||
PMID-9421169 Bilateral lesions of the subthalamic nucleus induce multiple deficits in an attentional task in rats.
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{1087} |
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]
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PMID-12477707[0] The cerebral oscillatory network of parkinsonian resting tremor.
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{1144} | |||||||||||||||||||||||||||||||
PMID-15242667 Anatomical funneling, sparse connectivity and redundancy reduction in the neural networks of the basal ganglia
PMID-15233923 Coincident but distinct messages of midbrain dopamine and striatal tonically active neurons.
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{1108} | |||||||||||||||||||||||||||||||
PMID-7983514[0] The Primate Subthalamic Nucleus. 1. Functional Properties in Intact Animals.
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{1143} | |||||||||||||||||||||||||||||||
PMID-11052216 Organization of the basal ganglia: the importance of axonal collateralization.
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{164} |
ref: DeLong-1985.02
tags: globus pallidus subthalamic STN electrophysiology Georgopoulos DeLong DBS
date: 02-24-2012 21:50 gmt
revision:5
[4] [3] [2] [1] [0] [head]
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PMID-3981228[0] Primate globus pallidus and subthalamic nucleus: functional organization
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{1142} | |||||||||||||||||||||||||||||||
PMID-4997823 Activity of Pallidal Neurons During Movement
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{1141} |
ref: -0
tags: putamen functional organization basal ganglia
date: 02-24-2012 21:01 gmt
revision:0
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PMID-6705861 Single cell studies of the primate putamen. I. Functional organization.
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{1138} | |||||||||||||||||||||||||||||||
PMID-1483512 Role of the primate basal ganglia and frontal cortex in the internal generation of movements. I. Preparatory activity in the anterior striatum
PMID-1483513 Role of primate basal ganglia and frontal cortex in the internal generation of movements. II. Movement-related activity in the anterior striatum.
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{168} | |||||||||||||||||||||||||||||||
PMID-7284825[0] Connections of the subthalamic nucleus in the monkey.
____References____
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{161} | |||||||||||||||||||||||||||||||
PMID-9464684[0] Physiological aspects of information processing in the basal ganglia of normal and parkinsonian primates.
____References____
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{828} |
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]
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PMID-11522580[0] The subthalamic nucleus in Parkinson's disease: somatotopic organization and physiological characteristics
Old notes:
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{1076} | |||||||||||||||||||||||||||||||
PMID-17017503[0] Synchronizing activity of basal ganglia and pathophysiology of Parkinson's disease.
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{159} | |||||||||||||||||||||||||||||||
PMID-21723919[0] Pathological basal ganglia activity in movement disorders.
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{1090} | |||||||||||||||||||||||||||||||
PMID-7711769[0] Functional anatomy of the basal ganglia. I. The cortico-basal ganglia-thalamo-cortical loop.
____References____
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{237} | |||||||||||||||||||||||||||||||
PMID-8783253[0] The subthalamic nucleus and the external pallidum: two tightly interconnected structures that control the output of the basal ganglia in the monkey.
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{169} |
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]
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PMID-14607789[0] The subthalamic nucleus in the context of movement disorders
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PMID-18614691[0] Role for subthalamic nucleus neurons in switching from automatic to controlled eye movement.
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PMID-7711765[0] Functional anatomy of the basal ganglia. II. The place of subthalamic nucleus and external pallidum in basal ganglia circuitry.
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PMID-20400953 Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics.
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PMID-16758482 "Paradoxical kinesis" is not a hallmark of Parkinson's disease but a general property of the motor system.
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PMID-20519543 Motor sequences and the basal ganglia: kinematics, not habits.
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PMID-20850966[0] Basal ganglia contributions to motor control: a vigorous tutor.
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PMID-17182916[0] Subthalamic and Striatal Neurons Concurrently Process Motor, Limbic, and Associative Information in Rats Performing an Operant Task
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{1116} | |||||||||||||||||||||||||||||||
IEEE-1580838 (pdf) Microfabricated cylindrical multielectrodes for neural stimulation.
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PMID-21270781 How advances in neural recording affect data analysis
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PMID-8836553[0] Single unit recording capabilities of a 100 microelectrode array. Nordhausen CT, Maynard EM, Normann RA.
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{1109} |
ref: -0
tags: Cogan 2008 electrodes recording stimulation
date: 02-05-2012 00:21 gmt
revision:0
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PMID-18429704 Neural stimulation and recording electrodes.
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PMID-19286561[0] Human Substantia Nigra Neurons Encode Unexpected Financial Rewards
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PMID-18562098[0] Accurate timing but increased impulsivity following excitotoxic lesions of the subthalamic nucleus.
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PMID-14960502[0] Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance.
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PMID-16973296[0] Subthalamic nucleus neurones in slices from MPTP-lesioned mice show irregular, dopamine-reversible firing pattern changes, but without synchronous activity
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PMID-15249649 Involvement of the human subthalamic nucleus in movement preparation
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PMID-10660885[0] Single-axon tracing study of neurons of the external segment of the globus pallidus in primate.
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PMID-7983515[0] The primate subthalamic nucleus. II. Neuronal activity in the MPTP model of parkinsonism
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{188} |
ref: neuro notes-0
tags: STN globus_pallidus striatum diagram basal_ganglia
date: 01-26-2012 17:16 gmt
revision:1
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http://www.gpnotebook.co.uk/cache/-1248198589.htm (bitrotted)
http://www.portfolio.mvm.ed.ac.uk/studentwebs/session1/group71/john.htm
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PMID-17962524[0] Hold your horses: impulsivity, deep brain stimulation, and medication in parkinsonism.
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{165} |
ref: Lehericy-2005.08
tags: fMRI motor_learning basal_ganglia STN subthalamic
date: 01-25-2012 00:20 gmt
revision:2
[1] [0] [head]
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PMID-16107540[0] Distinct basal ganglia territories are engaged in early and advanced motor sequence learning
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{12} |
ref: Breit-2006.1
tags: parkinsons basal_ganglia palladium substantia_nigra motor_control striate
date: 01-24-2012 22:10 gmt
revision:1
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I wish i could remember where i got these notes from, so as to verify the somewhat controversial statements. I found them written on the back of a piece of scrap paper.
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PMID-8849968[] Central Mechanisms of Tremor -- available through Duke's Ovid system. also in email.
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PMID-20505125[0] Deep brain stimulation alleviates parkinsonian bradykinesia by regularizing pallidal activity.
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{1084} | |||||||||||||||||||||||||||||||
PMID-19416950[0] Reward-learning and the novelty-seeking personality: a between- and within-subjects study of the effects of dopamine agonists on young Parkinson's patients
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{1085} | |||||||||||||||||||||||||||||||
PMID-21603228[0] Dopaminergic Balance between Reward Maximization and Policy Complexity.
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PMID-21096380[0] "A multi-channel low-power system-on-chip for single-unit recording and narrowband wireless transmission of neural signal."
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{255} |
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
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PMID-15013228[] Information processing, dimensionality reduction, and reinforcement learning in the basal ganglia (2003)
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{280} | |||||||||||||||||||||||||||||||
PMID-4966614[] Relation of pyramidal tract activity to force exerted during voluntary movement
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PMID-8036499[0] Direct cortical representation of drawing ____References____
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PMID-20161810[0] Bridging the Divide between Neuroprosthetic Design, Tissue Engineering and Neurobiology
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IEEE-5969351 (pdf) New class of chronic recording multichannel neural probes with post-implant self-deployed satellite recording sites | |||||||||||||||||||||||||||||||
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PMID-21719340 Modelization of a self-opening peripheral neural interface: a feasibility study. | |||||||||||||||||||||||||||||||
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IEEE-1201998 (pdf) A low-power low-noise CMOS amplifier for neural recording applications
Harrison, R.R. and Charles, C. A low-power low-noise CMOS amplifier for neural recording applications Solid-State Circuits, IEEE Journal of 38 6 958 - 965 (2003) | |||||||||||||||||||||||||||||||
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PMID-19199762[0] Optical Detection of Brain Cell Activity Using Plasmonic Gold Nanoparticles
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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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{1054} | |||||||||||||||||||||||||||||||
PMID-20089393[0] Electrical interfacing between neurons and electronics via vertically integrated sub-4 microm-diameter silicon probe arrays fabricated by vapor-liquid-solid growth.
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PMID-17913908[0] Single-Neuron Stability during Repeated Reaching in Macaque Premotor Cortex
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PMID-19067174[0] Integrated wireless neural interface based on the Utah electrode array
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{1049} | |||||||||||||||||||||||||||||||
IEEE-4353193 (pdf) A Sub-Microwatt Low-Noise Amplifier for Neural Recording
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{1050} | |||||||||||||||||||||||||||||||
IEEE-1643411 (pdf) A TinyOS-enabled MICA2-BasedWireless neural interface
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{1051} | |||||||||||||||||||||||||||||||
IEEE-5226763 (pdf) An Implantable 64-Channel Wireless Microsystem for Single-Unit Neural Recording
____References____ Sodagar, A.M. and Perlin, G.E. and Ying Yao and Najafi, K. and Wise, K.D. An Implantable 64-Channel Wireless Microsystem for Single-Unit Neural Recording Solid-State Circuits, IEEE Journal of 44 9 2591 -2604 (2009) | |||||||||||||||||||||||||||||||
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PMID-14757342[0] A multichannel telemetry system for single unit neural recordings
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{1055} | |||||||||||||||||||||||||||||||
IEEE-5230909 (pdf) A High Resolution Bi-Directional Communication through a Brain-Chip Interface
____References____ Maschietto, M. and Mahmud, M. and Stefano, G. and Vassanelli, S. Advanced Technologies for Enhanced Quality of Life, 2009. AT-EQUAL '09. 32 -35 (2009) | |||||||||||||||||||||||||||||||
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IEEE-4358095 (pdf) An Ultra-Low-Power Neural Recording Amplifier and its use in Adaptively-Biased Multi-Amplifier Arrays.
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{1025} | |||||||||||||||||||||||||||||||
IEEE-335862 (pdf) A three-dimensional microelectrode array for chronic neural recording.
____References____ Hoogerwerf, A.C. and Wise, K.D. A three-dimensional microelectrode array for chronic neural recording Biomedical Engineering, IEEE Transactions on 41 12 1136 -1146 (1994) | |||||||||||||||||||||||||||||||
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PMID-3957372[0] Solid-state electrodes for multichannel multiplexed intracortical neuronal recording.
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PMID-95711[0] Spike separation in multiunit records: A multivariate analysis of spike descriptive parameters
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{1037} | |||||||||||||||||||||||||||||||
IEEE-1546254 (pdf) A three-dimensional neural recording microsystem with implantable data compression circuitry
____References____ Olsson, R.H., III and Wise, K.D. A three-dimensional neural recording microsystem with implantable data compression circuitry Solid-State Circuits, IEEE Journal of 40 12 2796 - 2804 (2005) | |||||||||||||||||||||||||||||||
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PMID-6392757[0] Instruments for sorting neuroelectric data: a review
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{1041} | |||||||||||||||||||||||||||||||
IEEE-1052457 (pdf) A monolithic signal processor for a neurophysiological telemetry system
____References____ Dorman, M.G. and Prisbe, M.A. and Meindl, J.D. A monolithic signal processor for a neurophysiological telemetry system Solid-State Circuits, IEEE Journal of 20 6 1185 - 1193 (1985) | |||||||||||||||||||||||||||||||
{1042} | |||||||||||||||||||||||||||||||
IEEE-121568 (pdf) An implantable CMOS circuit interface for multiplexed microelectrode recording arrays
____References____ Ji, J. and Wise, K.D. ''An implantable CMOS circuit interface for multiplexed microelectrode recording arrays'' Solid-State Circuits, IEEE Journal of 27 3 433 -443 (1992) | |||||||||||||||||||||||||||||||
{1043} | |||||||||||||||||||||||||||||||
IEEE-1019051 (pdf) A multi channel chopper modulated neural recording system
____References____ Dagtekin, M. and Wentai Liu and Bashirullah, R. Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE 1 757 - 760 vol.1 (2001) | |||||||||||||||||||||||||||||||
{1044} | |||||||||||||||||||||||||||||||
PMID-10522821[0] A 100-channel system for real time detection and storage of extracellular spike waveforms.
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{985} | |||||||||||||||||||||||||||||||
IEEE-1185151 (pdf) Two multichannel integrated circuits for neural recording and signal processing
____References____ Obeid, I. and Morizio, J.C. and Moxon, K.A. and Nicolelis, M.A.L. and Wolf, P.D. Two multichannel integrated circuits for neural recording and signal processing Biomedical Engineering, IEEE Transactions on 50 2 255 -258 (2003) | |||||||||||||||||||||||||||||||
{316} | |||||||||||||||||||||||||||||||
PMID-12797724[0] A miniaturized neuroprosthesis suitable for implantation into the brain.
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{782} | |||||||||||||||||||||||||||||||
IEEE-5067358 (pdf) Wireless, Ultra Low Power, Broadband Neural Recording Microsystem
____References____ Song, Y.-K. and Borton, D.A. and Park, S. and Patterson, W.R. and Bull, C.W. and Laiwalla, F. and Mislow, J. and Simeral, J.D. and Donoghue, J.P. and Nurmikko, A.V. Active Microelectronic Neurosensor Arrays for Implantable Brain Communication Interfaces Neural Systems and Rehabilitation Engineering, IEEE Transactions on 17 4 339 -345 (2009) | |||||||||||||||||||||||||||||||
{911} | |||||||||||||||||||||||||||||||
PMID-19621062 Emergence of a stable cortical map for neuroprosthetic control.
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Below, scatter plots of cursor position for each of the 4 corner targets. That is, upper right plot was when the upper right target was present, etc. Data only from when the mk was paying attention. First plot: Dec 16 2011, 'early' 2D BMI session. Occupancy for each of the targets is about the same, with slightly higher occupancy for off-diagonal targets. Second plot: Dec 21 2011, 'less early' 2D BMI session. Occupancy is skewed for left and top right targets, but not for bottom right. Likely this is because he found it hard to reach this target, as neurons controlling X and Y directions were correlated. | |||||||||||||||||||||||||||||||
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PMID-18018699[0] HermesB: a continuous neural recording system for freely behaving primates.
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{779} | |||||||||||||||||||||||||||||||
PMID-16003903[0] Development of a chipscale integrated microelectrode/microelectronic device for brain implantable neuroengineering applications. -- second from this
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{930} | |||||||||||||||||||||||||||||||
IEEE-1300783 (pdf) Transmission latencies in a telemetry-linked brain-machine interface
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{318} | |||||||||||||||||||||||||||||||
PMID-14624244[0] Learning to control a brain-machine interface for reaching and grasping by primates.
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{308} | |||||||||||||||||||||||||||||||
IEEE-1214707 (pdf) Silicon-substrate intracortical microelectrode arrays for long-term recording of neuronal spike activity in cerebral cortex.
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PMID-19603074[0] Unscented Kalman filter for brain-machine interfaces.
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PMID-7409057[0] Operant control of precentral neurons: comparison of fast and slow pyramidal tract neurons.
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PMID-6769536[0] Operant control of precentral neurons: Control of modal interspike intervals
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PMID-4196269[0] Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles
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{303} | |||||||||||||||||||||||||||||||
PMID-4974291[0] Operant conditioning of cortical unit activity
PMID-5000088[1] Operant conditioning of specific patterns of neural and muscular activity. In awake monkeys we recorded activity of single "motor" cortex cells, four contralateral arm muscles, and elbow position, while operantly reinforcing several patterns of motor activity. With the monkey's arm held semiprone in a cast hinged at the elbow, we reinforced active elbow movements and tested cell responses to passive elbow movements. With the cast immobilized we reinforced isometric contraction of each of the four muscles in isolation, and bursts of cortical cell activity with and without simultaneous suppression of muscle activity. Correlations between a precentral cell and specific arm muscles consistently appeared under several behavioral conditions, but could be dissociated by reinforcing cell activity and muscle suppression. PMID-4624487[2] Operant conditioning of isolated activity in specific muscles and precentral cells Recorded precentral units in monkeys, trained to contract 4 arm muscles in isolation, under various conditions: passive movements and cutaneous stimulation, active movements and isometric contractions. Some Ss were also reinforced for activity of cortical cells, with no contingency in muscle activity and with simultaneous suppression of all muscular activity. It is concluded that temporal correlations between activity of precentral cells and some other component of the motor response, e.g., muscle activity, force, or position, may depend as strongly on the specific response pattern which is reinforced as on any underlying physiological connection. ____References____ | |||||||||||||||||||||||||||||||
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IEEE-1484848 (pdf) A high-yield IC-compatible multielectrode recording array.
____References____ Najafi, K. and Wise, K.D. and Mochizuki, T. A high-yield IC-compatible multichannel recording array Electron Devices, IEEE Transactions on 32 7 1206 - 1211 (1985) | |||||||||||||||||||||||||||||||
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PMID-6077726[0] The limbic system and behavioral reinforcement
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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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http://infinite-interface.net/ -- a neuroscientist at University of Southern California. Many thoughtful, informative posts. | |||||||||||||||||||||||||||||||
{368} |
ref: thesis-0
tags: clementine 051607 operant conditioning tlh24
date: 01-06-2012 03:09 gmt
revision:1
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the cells were, basically, as usual for today. did 1-d BMI on channel 29; worked somewhat (nothing dramatic; mk is out of practice?) | |||||||||||||||||||||||||||||||
{425} | |||||||||||||||||||||||||||||||
images/425_1.pdf August 2007
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from the book "Neural Prostheses for Restoration of Sensory and Motor Function" edited by John Chapin and Karen Moxon. Phillip Kennedy's one-channel neurotrophic glass electrode BMI (axons apparently grew into the electrode, and he recorded from them) Pat Wolf on neural amplification / telemetry technology battery technology for powering the neural telemetry | |||||||||||||||||||||||||||||||
{349} |
ref: thesis-0
tags: clementine 042007 operant conditioning biofeedback tlh24
date: 01-06-2012 03:08 gmt
revision:4
[3] [2] [1] [0] [head]
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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: | |||||||||||||||||||||||||||||||
{351} | |||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||
{249} |
ref: notes-0
tags: sorting SNR correlation coefficient expectation maximization tlh24
date: 01-06-2012 03:07 gmt
revision:5
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Description: red is the per-channel cross-validated correlation coeifficent of prediction. Blue is the corresponding number of clusters that the unit was sorted into, divided by 10 to fit on the same axis. The variable being predicted is cartesian X position. note 32 channels were dead (from PP). The last four (most rpedictive) channels were: 71 (1 unit), 64 (5 units), 73 (6 units), 67 (1 unit). data from sql entry: clem 2007-03-08 18:59:27 timarm_log_20070308_185706.out ;Looks like this data came from PMD region. Description: same as above, but for the y-axis. Description: same as above, but for the z-axis. Conclusion: sorting seems to matter & have a non-negligible positive effect on predictive ability. | |||||||||||||||||||||||||||||||
{175} | |||||||||||||||||||||||||||||||
{1034} |
ref: Towe-2007.05
tags: RF recording passive backscatter variactors
date: 01-06-2012 02:56 gmt
revision:3
[2] [1] [0] [head]
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IEEE-4227238 (pdf) Passive Backscatter Biotelemetry for Neural Interfacing
IEEE-5993487 (pdf) A Fully Passive Wireless Microsystem for Recording of Neuropotentials Using RF Backscattering Methods
____References____ Towe, B.C. Neural Engineering, 2007. CNE '07. 3rd International IEEE/EMBS Conference on 144 -147 (2007) | |||||||||||||||||||||||||||||||
{1007} | |||||||||||||||||||||||||||||||
IEEE-5910570 (pdf) Spiking neural network decoder for brain-machine interfaces
____References____ Dethier, J. and Gilja, V. and Nuyujukian, P. and Elassaad, S.A. and Shenoy, K.V. and Boahen, K. Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on 396 -399 (2011) | |||||||||||||||||||||||||||||||
{888} | |||||||||||||||||||||||||||||||
Experiment: you have a key. You want that key to learn to control a BMI, but you do not want the BMI to learn how the key does things, as
Given this, I propose a very simple groupweight: one axis is controlled by the summed action of a certain population of neurons, the other by a second, disjoint, population; a third population serves as control. The task of the key is to figure out what does what: how does the firing of a given unit translate to movement (forward model). Then the task during actual behavior is to invert this: given movement end, what sequence of firings should be generated? I assume, for now, that the brain has inbuilt mechanisms for inverting models (not that it isn't incredibly interesting -- and I'll venture a guess that it's related to replay, perhaps backwards replay of events). This leaves us with the task of inferring the tool-model from behavior, a task that can be done now with our modern (though here-mentioned quite simple) machine learning algorithms. Specifically, it can be done through supervised learning: we know the input (neural firing rates) and the output (cursor motion), and need to learn the transform between them. I can think of many ways of doing this on a computer:
{i need to think more about model-building, model inversion, and songbird learning?} | |||||||||||||||||||||||||||||||
{949} | |||||||||||||||||||||||||||||||
PMID-18509337[0] Cortical control of a prosthetic arm for self-feeding
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{990} | |||||||||||||||||||||||||||||||
PMID-19464514[0] Three-dimensional, automated, real-time video system for tracking limb motion in brain-machine interface studies.
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{97} | |||||||||||||||||||||||||||||||
From Uncertain Spikes to Prosthetic Control a powerpoint presentation w/ good overview of all that the Brown group has done | |||||||||||||||||||||||||||||||
{309} | |||||||||||||||||||||||||||||||
PMID-10776811[0] More than a year of recording with up to 64 microelectrodes
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{1031} | |||||||||||||||||||||||||||||||
PMID-17215384[0] Comparison of recordings from microelectrode arrays and single electrodes in the visual cortex.
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{995} | |||||||||||||||||||||||||||||||
IEEE-936367 (pdf) Single-unit neural recording with active microelectrode arrays
____References____ Qing Bai and Wise, K.D. Single-unit neural recording with active microelectrode arrays Biomedical Engineering, IEEE Transactions on 48 8 911 -920 (2001) | |||||||||||||||||||||||||||||||
{996} | |||||||||||||||||||||||||||||||
IEEE-1052646 (pdf) An implantable multielectrode array with on-chip signal processing
____References____ Najafi, K. and Wise, K.D. An implantable multielectrode array with on-chip signal processing Solid-State Circuits, IEEE Journal of 21 6 1035 - 1044 (1986) | |||||||||||||||||||||||||||||||
{330} | |||||||||||||||||||||||||||||||
PMID-13969854[0] Control and Training of Individual Motor Units
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PMID-4207598[0] Behavioral control of firing patterns of normal and abnormal neurons in chronic epileptic cortex.
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{1016} | |||||||||||||||||||||||||||||||
bibtex: Lilly-1958 Correlations between Neurophysiological Activity in the Cortex and Short-Term Behavior in the Monkey
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{313} | |||||||||||||||||||||||||||||||
PMID-12960378 Chronic, multisite, multielectrode recordings in macaque monkeys.
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{1011} | |||||||||||||||||||||||||||||||
IEEE-4120642 (pdf) Mechanical Factors in the Design of Chronic Recording Intracortical Microelectrodes ____References____ Goldstein, Seth R. and Salcman, Michael Mechanical Factors in the Design of Chronic Recording Intracortical Microelectrodes Biomedical Engineering, IEEE Transactions on BME-20 4 260 -269 (1973) | |||||||||||||||||||||||||||||||
{1013} | |||||||||||||||||||||||||||||||
PMID-17793797[0] Tungsten Microelectrode for Recording from Single Units.
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{281} | |||||||||||||||||||||||||||||||
PMID-4977837[0] Activity of Pyramidal Tract neurons during postural fixation
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{288} | |||||||||||||||||||||||||||||||
PMID-11240278[0] Functions of mammalian spinal interneurons during movement
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{393} | |||||||||||||||||||||||||||||||
PMID-17554826[0] A fully integrated mixed-signal neural processor for implantable multichannel cortical recording.
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{902} | |||||||||||||||||||||||||||||||
bibtex:Olson-2005 Evidence of a mechanism of neural adaptation in the closed loop control of directions
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{739} | |||||||||||||||||||||||||||||||
PMID-2345003[0] Strength characterization of silicon microprobes in neurophysiological tissues.
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{1014} |
ref: GULD-1964.07
tags: platinum iridium microelectrode eltrolytic etching original
date: 01-03-2012 19:05 gmt
revision:2
[1] [0] [head]
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PMID-14199966[0] A Glass-covered platinum microelectrode
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{841} | |||||||||||||||||||||||||||||||
PMID-20705858[0] Three-Dimensional, Flexible Nanoscale Field-Effect Transistors as Localized Bioprobes
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{1004} | |||||||||||||||||||||||||||||||
IEEE-1351853 (pdf) Development of integrated circuits for readout of microelectrode arrays to image neuronal activity in live retinal tissue
____References____ Dabrowski, W. and Grybos, P. and Hottowy, P. and Skoczen, A. and Swientek, K. and Bezayiff, N. and Grillo, A.A. and Kachiguine, S. and Litke, A.M. and Sher, A. Nuclear Science Symposium Conference Record, 2003 IEEE 2 956 - 960 Vol.2 (2003) | |||||||||||||||||||||||||||||||
{972} |
ref: Bures-1968
tags: inferior colliculus stimulation classical conditioning plasticity hebb Bures
date: 01-03-2012 07:08 gmt
revision:5
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bibtex:Bures-1968 Plastic changes of unit activity based on reinforcing properties of extracellular stimulation of single neurons
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{366} | |||||||||||||||||||||||||||||||
PMID-17271187[0] Dynamic control of extracellular environment in in vitro neural recording systems.
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{224} |
ref: notes-0
tags: k-means clustering neurophysiology sorting
date: 01-03-2012 06:51 gmt
revision:1
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k-means is easy! % i want to do the k-means alg. v = [x y]; nc = 7; dim = cols(v); n = rows(v); cent = rand(nc,dim); d = zeros(n, nc); for k = (1:500) for s = 1:nc d(:,s) = sqrt(sum((v - rvecrep(cent(s, :), n)).^2,2)); end % select the smallest [nada, g] = min(d'); g = g'; for s = 1:nc if(numel(find(g==s)) > 0) cent(s, :) = mean(v(g==s, :)); end end end real data from clementine: | |||||||||||||||||||||||||||||||
{261} | |||||||||||||||||||||||||||||||
PMID-17360898[] Relationship between Unconstrained Arm Movements and Single-Neuron Firing in the Macaque Motor Cortex
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{96} | |||||||||||||||||||||||||||||||
PMID-10561437[0] Motor cortical representation of speed and direction during reaching
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{595} | |||||||||||||||||||||||||||||||
PMID-8294972 Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys. 1993
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{778} | |||||||||||||||||||||||||||||||
PMID-2340869[0] Dynamic organization of primary motor cortex output to target muscles in adult rats. II. Rapid reorganization following motor nerve lesions.
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{833} | |||||||||||||||||||||||||||||||
IEEE-4358608 (pdf) An Integrated System for Simultaneous, Multichannel Neuronal Stimulation and Recording
Blum RA, Ross JD Brown EA and DeWeerth SP (2007) An Integrated System for Simultaneous, Multichannel Neuronal Stimulation and Recording IEEE Trans. Circuits Syst. I. Regular Pap 54, 2608-2618 | |||||||||||||||||||||||||||||||
{959} | |||||||||||||||||||||||||||||||
PMID-4966614 Relation of pyramidal tract activity to force exerted during voluntary movement.
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{149} | |||||||||||||||||||||||||||||||
IEEE-01258173 (pdf) Wireless implantable microsystems: high-density electronic interfaces to the nervous system - January 2004.
____References____ WISE, K.D. and ANDERSON, D.J. and HETKE, J.F. and KIPKE, D.R. and NAJAFI, K. Wireless implantable microsystems: high-density electronic interfaces to the nervous system Proceedings of the IEEE 92 1 76 - 97 (2004) | |||||||||||||||||||||||||||||||
{312} | |||||||||||||||||||||||||||||||
PMID-12904510[0] Csicsvari 2003 Massively parallel recording of unit and local field potentials with silicon-based electrodes
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{978} | |||||||||||||||||||||||||||||||
PMID-5683678[0] Intracerebral radio stimulation and recording in completely free patients.
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{150} |
ref: Otto-2006.02
tags: electrophysiology recording rejuvenation stimulation MEA
date: 01-03-2012 03:21 gmt
revision:3
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PMID-16485763[0] Voltage pulses change neural interface properties and improve unit recordings with chronically implanted microelectrodes.
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{928} |
ref: Kennedy-1989.09
tags: Kennedy neurotrophic electrode recording fabrication 1989 electrophysiology
date: 01-03-2012 03:21 gmt
revision:2
[1] [0] [head]
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PMID-2796391[0] The cone electrode: a long-term electrode that records from neurites grown onto its recording surface.
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{310} | |||||||||||||||||||||||||||||||
PMID-10592339[0] Long term neural recording characteristics of wire microelectrode arrays implanted in cerebral cortex
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{830} | |||||||||||||||||||||||||||||||
PMID-19668698[0] A low-cost multielectrode system for data acquisition enabling real-time closed-loop processing with rapid recovery from stimulation artifacts
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{788} |
ref: -0
tags: reinforcement learning basis function policy specialization
date: 01-03-2012 02:37 gmt
revision:1
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To read: | |||||||||||||||||||||||||||||||
{630} | |||||||||||||||||||||||||||||||
PMID-16543459[0] Reward Timing in the Primary Visual Cortex
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{687} | |||||||||||||||||||||||||||||||
PMID-18229536[0] Effect of mental imagery on the development of skilled motor actions.
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{336} | |||||||||||||||||||||||||||||||
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.
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{623} | |||||||||||||||||||||||||||||||
Reinforcement learning in the cortex (a web scour/crawl):
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{327} | |||||||||||||||||||||||||||||||
PMID-6253605[0] Functional classes of primate corticomotoneuronal cells and their relation to active force
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{299} | |||||||||||||||||||||||||||||||
PMID-7760138[0] Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons
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{376} | |||||||||||||||||||||||||||||||
IEEE-1419566 (pdf) A Portable Wireless DSP System for a Brain Machine Interface
____References____ Darmanjian, S. and Morrison, S. and Dang, B. and Gugel, K. and Principe, J. Neural Engineering, 2005. Conference Proceedings. 2nd International IEEE EMBS Conference on 112 -115 (2005) | |||||||||||||||||||||||||||||||
{731} | |||||||||||||||||||||||||||||||
PMID-15132510[0] A fully Integrated Neural Recording Amplifier with DC Input Stabilization
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{743} | |||||||||||||||||||||||||||||||
PMID-17260864[0] An integrated system for multichannel neuronal recording with spike/LFP separation, integrated A/D conversion and threshold detection.
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{744} |
ref: Merletti-2009.02
tags: surface EMG multielectrode recording technology italy
date: 01-03-2012 01:07 gmt
revision:2
[1] [0] [head]
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PMID-19042063[0] Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art
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{893} | |||||||||||||||||||||||||||||||
PMID-21880826[0] http://cshprotocols.cshlp.org/content/2011/9/pdb.prot065474.full?rss=1
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{894} | |||||||||||||||||||||||||||||||
IEEE-5619710 (pdf) A Multi-Channel Low-Power IC for Neural Spike Recording with Data Compression and Narrowband 400-MHz MC-FSK Wireless Transmission
____References____ Bonfanti, A. and Ceravolo, M. and Zambra, G. and Gusmeroli, R. and Borghi, T. and Spinelli, A.S. and Lacaita, A.L. ESSCIRC, 2010 Proceedings of the 330 -333 (2010) | |||||||||||||||||||||||||||||||
{910} | |||||||||||||||||||||||||||||||
PMID-11491[0] Afferent input to movement-related precentral neurones in conscious monkeys.
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{665} |
ref: Cho-2007.03
tags: SOM self organizing maps Prinicpe neural signal reconstruction recording compression
date: 01-03-2012 00:59 gmt
revision:2
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PMID-17234384[0] Self-organizing maps with dynamic learning for signal reconstruction.
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{937} | |||||||||||||||||||||||||||||||
PMID-19255459[0] A fully implantable 96-channel neural data acquisition system.
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{982} | |||||||||||||||||||||||||||||||
PMID-6492861[0] A simple method for the construction of electrode arrays.
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{986} | |||||||||||||||||||||||||||||||
PMID-12367642[0] Multielectrode recordings: the next steps.
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{994} | |||||||||||||||||||||||||||||||
PMID-8351520[0] Dynamics of the hippocampal ensemble code for space.
PMID-8036517[1] Reactivation of hippocampal ensemble memories during sleep.
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{741} | |||||||||||||||||||||||||||||||
IEEE-4463150 (pdf) A neural signal processor for an implantable multi-channel cortical recording microsystem
____References____ Sodagar, A.M. and Wise, K.D. and Najafi, K. Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE 5900 -5903 (2006) | |||||||||||||||||||||||||||||||
{1001} | |||||||||||||||||||||||||||||||
IEEE-5335132 (pdf) Low-cost wireless neural recording system and software
____References____ Gregory, J.A. and Borna, A. and Roy, S. and Xiaoqin Wang and Lewandowski, B. and Schmidt, M. and Najafi, K. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE 3833 -3836 (2009) | |||||||||||||||||||||||||||||||
{873} | |||||||||||||||||||||||||||||||
PMID-21240274[0] A wireless multi-channel neural amplifier for freely moving animals.
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{1002} |
ref: Fan-2011.01
tags: TBSI wireless recordings system FM modulation multiplexing poland
date: 01-03-2012 00:55 gmt
revision:5
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PMID-21765934[0] A wireless multi-channel recording system for freely behaving mice and rats.
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{1003} | |||||||||||||||||||||||||||||||
IEEE-5333227 (pdf) In vivo testing of a low noise 32-channel wireless neural recording system
____References____ Ming Yin and Seung Bae Lee and Ghovanloo, M. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE 1608 -1611 (2009) | |||||||||||||||||||||||||||||||
{1005} | |||||||||||||||||||||||||||||||
IEEE-5471737 (pdf) HermesD: A High-Rate Long-Range Wireless Transmission System for Simultaneous Multichannel Neural Recording Applications
____References____ Miranda, H. and Gilja, V. and Chestek, C.A. and Shenoy, K.V. and Meng, T.H. HermesD: A High-Rate Long-Range Wireless Transmission System for Simultaneous Multichannel Neural Recording Applications Biomedical Circuits and Systems, IEEE Transactions on 4 3 181 -191 (2010) | |||||||||||||||||||||||||||||||
{1006} | |||||||||||||||||||||||||||||||
IEEE-5061585 (pdf) Wireless Neural Recording With Single Low-Power Integrated Circuit
____References____ Harrison, R.R. and Kier, R.J. and Chestek, C.A. and Gilja, V. and Nuyujukian, P. and Ryu, S. and Greger, B. and Solzbacher, F. and Shenoy, K.V. Wireless Neural Recording With Single Low-Power Integrated Circuit Neural Systems and Rehabilitation Engineering, IEEE Transactions on 17 4 322 -329 (2009) | |||||||||||||||||||||||||||||||
{365} | |||||||||||||||||||||||||||||||
IEEE-717081 (pdf) An Implantable Multichannel Digital neural recording system for a micromachined sieve electrode
____References____ 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) | |||||||||||||||||||||||||||||||
{663} | |||||||||||||||||||||||||||||||
PMID-19162894[0] Implementation of a telemetry system for neurophysiological signals.
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{291} | |||||||||||||||||||||||||||||||
PMID-16291944[0] Stable ensemble performance with single-neuron variability during reaching movements in primates.
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{5} |
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
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hunch.net interesting posts:
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PMID-17187065[0] Separate neural substrates for skill learning and performance in the ventral and dorsal striatum.
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{314} | |||||||||||||||||||||||||||||||
PMID-14757341[1] A low power multichannel analog front end for portable neural signal recordings.
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{963} | |||||||||||||||||||||||||||||||
PMID-4598035[0] Operant conditioning of single-unit response patterns in visual cortex.
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{964} |
ref: OLDS-1954.12
tags: Olds Milner operant conditioning electrical reinforcement wireheading BMI
date: 12-29-2011 05:09 gmt
revision:5
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PMID-13233369[0] Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain.
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{621} |
ref: Ativanichayaphong-2008.05
tags: wireless neural recording stimulation
date: 12-28-2011 21:15 gmt
revision:3
[2] [1] [0] [head]
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PMID-18262282[0] A combined wireless neural stimulating and recording system for study of pain processing
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{719} | |||||||||||||||||||||||||||||||
I was looking over the NIH's omnibus document (181 pages!) for the Challenge Grants due April 27, and happened upon a few interesting & relevant ones - and made some observations along the way.
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{924} | |||||||||||||||||||||||||||||||
PMID-5941514[0] Feeding induced in cats by electrical stimulation of the brain stem.
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{834} | |||||||||||||||||||||||||||||||
IEEE-4464125 (pdf) Stimulus-Artifact Elimination in a Multi-Electrode System
Brown EA, Ross JD, Blum RA, Yoonkey N, Wheeler BC, and DeWeerth SP (2008) Stimulus-Artifact Elimination in a Multi-Electrode System. IEEE TRans. Biomed. Circuit Sys. 2. 10-21 | |||||||||||||||||||||||||||||||
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PMID-10196571[0] Simultaneous encoding of tactile information by three primate cortical areas
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{968} |
ref: Bassett-2009.07
tags: Weinberger congnitive efficiency beta band neuroimagaing EEG task performance optimization network size effort
date: 12-28-2011 20:39 gmt
revision:1
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PMID-19564605[0] Cognitive fitness of cost-efficient brain functional networks.
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PMID-11017160 Reply to One motor cortex, two different views | |||||||||||||||||||||||||||||||
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PMID-6396456[0] Computer separation of multi-unit neuroelectric data: a review
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{939} |
ref: -0
tags: Georgopoulos 1988 population vector tuning
date: 12-20-2011 01:13 gmt
revision:1
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PMID-3411362[0] Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population.
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{938} | |||||||||||||||||||||||||||||||
PMID-3411363[0] Primate motor cortex and free arm movements to visual targets in three-dimensional space. III. Positional gradients and population coding of movement direction from various movement origins.
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{936} | |||||||||||||||||||||||||||||||
PMID-3411361[0] Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement.
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{935} |
ref: Georgopoulos-1982.11
tags: Georgopoulos 1982 motor tuning cortex M1 population vector
date: 12-19-2011 23:52 gmt
revision:1
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PMID-7143039 On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.
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{267} |
ref: Kennedy-1992.08
tags: BMI Kennedy cone electrode electrophysiology recording neurotrophic
date: 12-17-2011 01:00 gmt
revision:1
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PMID-1407726[] The cone electrode: ultrastructural studies following long-term recording in rat and monkey cortex
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{65} | |||||||||||||||||||||||||||||||
follow up paper: http://spikelab.jbpierce.org/Publications/LaubachEMBS2003.pdf
____References____ Laubach, M. and Arieh, Y. and Luczak, A. and Oh, J. and Xu, Y. Bioengineering Conference, 2003 IEEE 29th Annual, Proceedings of 17 - 18 (2003.03) | |||||||||||||||||||||||||||||||
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PMID-19386759[0] Wireless neural stimulation in freely behaving small animals.
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{921} | |||||||||||||||||||||||||||||||
PMID-16102841[0] An autonomous implantable computer for neural recording and stimulation in unrestrained primates.
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{69} | |||||||||||||||||||||||||||||||
PMID-17057705 Long-term motor cortex plasticity induced by an electronic neural implant.
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{916} | |||||||||||||||||||||||||||||||
To stream a webcam directly -- no transcoding (this is useful in the case that you have a direct, fast connection between the source and sink), do the following: cvlc v4l2:///dev/video0 --sout '#standard{access=http,mux=ts,dst=:1234}' To see this on a webpage, make a file localstream.asx and put in it: <ASX version ="3.0"> <TITLE>Stream1234</TITLE> <ENTRY> <REF HREF="http://lovely.local:1234" /> </ENTRY> </ASX> (where lovely.local is replaced by your machine name). Then link to it or embed this file in a webpage! Note: I have been unable to stream more than one camera, but perhaps this is because they are both on the same USB hub. Could also be that they are both cheap ($7) pieces of shit, but that's why I bought 2. | |||||||||||||||||||||||||||||||
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PMID-9658025[0] Predictive reward signal of dopamine neurons.
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{156} | |||||||||||||||||||||||||||||||
PMID-12040201[0] Anterior cingulate: single neuronal signals related to degree of reward expectancy
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{154} |
ref: OReilly-2006.02
tags: computational model prefrontal_cortex basal_ganglia
date: 12-07-2011 04:11 gmt
revision:1
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PMID-16378516[0] Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia found via: http://www.citeulike.org/tag/basal-ganglia ____References____
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PMID-16212764[0] Incremental online learning in high dimensions ideas:
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PMID-15537672[0] On the Benefits of not Trying: Brain Activity and Connectivity Reflecting the Interactions of Explicit and Implicit Sequence Learning quote: ünder certain curcumstances, automatic learning may be attenuated by explicit memory processes" : expicit attemps to learn a difficult sequence (compared to a control) produces a failure in implicit learning, and this failure is caused by the supression of learning rather than the expression. There is a deleterious effect of explicit search on implicit learning.
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{300} | |||||||||||||||||||||||||||||||
Motor learning by field approximation.
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{323} |
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]
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PMID-17008410[0] Operant matching is a generic outcome of synaptic plasticity based on the covariance between reward and neural activity
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{252} | |||||||||||||||||||||||||||||||
PMID-15022843[0] A simulation study of information transmission by multi-unit microelectrode recordings key idea:
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From Scott MacKenzie:
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{699} |
ref: Harris-2008.03
tags: retroaxonal retrosynaptic Harris learning cortex backprop
date: 12-07-2011 02:34 gmt
revision:2
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PMID-18255165[0] Stability of the fittest: organizing learning through retroaxonal signals
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PMID-8670641[0] The hippocampo-neocortical dialogue.
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Timetable / Plan:
Contingency Plan:
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{723} |
ref: notes-0
tags: data effectiveness Norvig google statistics machine learning
date: 12-06-2011 07:15 gmt
revision:1
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The unreasonable effectiveness of data.
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PMID-6772272 Operant control of precentral neurons: bilateral single unit conditioning.
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PMID-4041789 Synchrony between cortical neurons during operant conditioning.
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PMID-10681435 Cortical correlates of learning in monkey adapting to a new dynamical environment. | |||||||||||||||||||||||||||||||
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PMID-7140894 Short-term changes in cell activity of areas 4 and 5 during operant conditioning.
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PMID-4598035 Operant conditioning of single-unit response patterns in visual cortex
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Open letter proposing some ideas on how to automate programming: simulate a human! Rather from a neuro background, and rather sketchy (as in vague, not as in the present slang usage). | |||||||||||||||||||||||||||||||
{872} | |||||||||||||||||||||||||||||||
Excellent hike in Bynum NC starting at the old homestead down there, crossed a number of random properties, entered and left Haw river state park, saw a good number of decomposing farmhouses, all on a gorgeous day. Route was taken clockwise; jog at the end away from main trail was to avoid a hunter in the main fields. This forced us to do a good bit of bushwacking and gave the opportunity to meet some local horses, goats, and runners. Total distance about 9 miles.
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http://www.autonlab.org/tutorials/ -- excellent http://energyfirefox.blogspot.com/2010/12/data-mining-with-ubuntu.html -- apt-get! | |||||||||||||||||||||||||||||||
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The goal is to make a system that is capable of automatic debugging / design. It seems to me that a good bit of the work of designing and debugging such a program - which I imagine to be basically a set of heuristics triggered by certain situations - is mechanical, and hence could be automated. First, debugging: Say your program doesn't compile. You look at the error message + location, look at the code, and change variables either there or in causally-connected code to fix the error. The knowledge of what to change basically involves a forward model of how the computer runs the program and experiential knowledge of what worked in the past. The former need not be encoded - the computer can simply run slightly different versions of the program and see what happens itself (aka 'taking the partial derivatives of a program'). The latter can be stored in a well-designed database. Then your program crashes. Usually, you look at the code and run it on a model of the computer in your mind, checking at every point if the given data-path and code-path to see if it diverges from acceptable behavior. Maybe you instrument the code with printfs. Then, you look at the causally-connected code to see what can be changed to alleviated the error condition, or to make the code/datapath align with well-known programming patterns. Again, the computer does not need a model of itself - the computer can simply running the code as an interpreted language; everything is then instrumented. The well-known patterns can be entered by a human, or learned via experimentation (analogous to 'school' and 'hacking' to a person). Now your program runs and compiles. But, it doesn't do what you want it to do - you see in one particular case there is an error. So, you look at the program, run it in your mind, and look at all the variables and codepaths that are causally related to producing that case, imagining what changes to each will do to the error. Possibly this involves changing the AST, adding an if-statement, etc. You poke around; eventually something works, if partially. The computer can do the same thing, via the well-instrumented interpreted language. Eventually this produces spaghetti code (unless you're really smart, and predicted all this case-programming), and small tweaks break more things than they fix. Time to refactor - apply well-known invariant code transformations, or more generally look for simpler codepaths with the same effective datapaths. Perhaps you avoided it by using good initial programming patterns, found mostly through analogy with the real world (object-orientation, MVC etc). Again this is where a good memory of programming-patterns will serve well. That's the gist. There is much more - namely, the importance of finding transforms that render problems and sub-problems linear, and the practical consideration that the end goal in much computer programming is itself found through iteration - but this email is long enough. What I've outlined probably doesn't look close to GA / EA, but in practice will most likely involve intense and random experimentation within each of the steps above, something that GA does a lot of. What should make it faster is that individual random experimentation is more constrained - smaller space to explore - and when possible memory can replace expending time and power on experimentation. To look at it another way, the project should move a feedback loop (write compile observe) from the human to the computer; this is OK as all the needed info is within the computer already; no outside interaction is needed (unlike, say physical evolution). | |||||||||||||||||||||||||||||||
{867} |
ref: -0
tags: evolutionary psychology human mating sexuality discrimination wedlock
date: 01-09-2011 18:22 gmt
revision:1
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From Why Beautiful people have more daughters: "Abuse, degradation, and intimidation are all part of men's unfortunate repertoire of tactics employed in competitive situations. In other words, men are not harassing women because they are treating them differently than men (which is the definition of discrimination under which harassment legally falls), but the exact opposite: men harass women because they are not discriminating between men and women." Interesting argument. But in sexual discrimination cases, the women are not being treated the way they want to be treated - this is more a problem than the inequality. The author then goes on to pose that current sexual discrimination law and policy in US corporations actually inhibits welcome sexual/romantic interest/advances. Many people do find partners at work. Again, I beg to differ: if there is passion between people, things will fall as they should; if policy and culture serves to make this more civilized (provided it's not completely inhibited, as the author suggests), then all the better. In related news: An Analysis of Out-Of-Wedlock Births in the United States Central hypothesis: Contraceptive technology shifted the balance of power between the sexes: prior the pill, women could force the men into promising to marry; in the case of preganancy, cultural standards forced marriage - shotgun marriage. Men accepted these terms because they were uniform across all women - sex implies pregnancy implies child rearing. When contraception became available, this was decoupled, as sex did not beget pregnancy; those women who negotiated on the old terms were likely to lose their mate, hence shotgun marriages (the result of such negotiations) gradually disappeared from culture. The author generally approves of the idea of shotgun marriage, and suggests that a governmental body should enforce a form of it through child support payments. Presently about 40% of children in the US are born out of wedlock. Finally, Serial monogamy increases reproductive success in men but not in women. It rests upon data, only recently gathered, that supports that having multiple partners increases reproductive success more strongly in male than in female humans. This implies that the variance of the fertility of men should be higher than that of women - again, which is borne out in the data, but only weakly: men have 10% higher variance in # of offspring than women. This effect is correlated to serial monogamy - "Compared with men with 1 spouse, men with 3 or more spouses had 19% more children in the total sample". This did not hold with women, nor did varying spouse number in men change the survival rate of their offspring. Irregardless, this reading was spurred by someone mentioning that a genetic analysis of human populations reveals that while 80% of women reached reproductive success, only 40% of men did - implying that historically a few more successful men fathered a large fraction of children. I was unable to find evidence to support this on the internet (and indeed the Behavioral Ecology article gives much less dramatic figures), but it makes intuitive sense, especially in light of some patterns of male behavior. | |||||||||||||||||||||||||||||||
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This evening, on the drive back from wacky (and difficult) Russian-style yoga, I got a chance to explain to my brother what I really want to be working on, the thing that really tickles my fancy. My brother and I, so much as genetic commonality and common upbringing seem to effect, have very similar styles of thinking, which made explaining things a bit easier. For you, dear readier, I'll expand a bit. I'd like to write a program that writes other programs, iteratively, given some objective function / problem statement / environment in which to interact. The present concrete goal is to have a said program make a program that is able to lay out PCBs with quality similar to that of humans. The overarching framework that I'm planning on using is genetic/evolutionary algorithms (the latter does not have crossover, fyi), but no one has applied GA to the problem in this way: most people use GA to solve a particular instance of a problem. Rubbish, i say, this is energy wasteful! Rubbish, you may return: the stated problem requires a degree of generalization and disconnect from the 'real world' (the PCB) that makes GAs extremely unlikely to come up with any solutions. Expressed another way: the space to be explored is too large (program begets program begets solution). This is a very sensible critique; there is no way in hell a GA can solve this problem. They are notably pathetic at exploring space in a energy-efficient way (to conclude a paragraph again with energy... ). There are known solutions for this: memory -- cache the results, in terms of algorithm & behavior, of all 'hypotheses' or individuals tried out by a GA. This is what humans do -- they remember the results of their experiment, and substitute the result rather than running a test again. But humans do something far more sophisticated and interesting than just memory - they engineer systems; engineering is an iterative process that often goes down wrong design paths, yet it nonetheless delivers awesome things like Saabs and such. As I described to K--, engineering is not magic and can be (has been?) described mechanistically. First of all, most engineering artifacts start off from established, well-characterized components, aggregated through the panoply of history. Some of these components describe how other components are put together, things that are either learned in school or by taking things apart. Every engineer, ala Newton, stands on the vast shoulders of the designers before; hence any program must also have these shoulders available. The components are assembled into a system in a seemingly ad-hoc and iterative procedure: sometimes you don't know what you want, so you play with the parts sorta randomly, and see what interesting stuff comes out. Other times you know damn well what you / your boss / the evil warlord who holds you captive wants. Both modes are interesting (and the dichotomy is artificial), but the latter is more computer-like, hence to be modeled. Often the full details of the objective function or desired goal is very unclear in the hands of the boss / evil warlord (1), despite how reluctant they may be to admit this. Such an effect is well documented in Fred Brooks' book, __The Design of Design__. Likewise, how to get to a solution is unclear in the mind of an engineer, so he/she shuffles things around in the mind (2),
This search is applied iteratively, apparently a good bit of the time subconsciously. A component exists in our mind as a predictive model of how the thing behaves, so we simulate it on input, observe output, and check to see if anything there is correlated / decorrelated with target features. (One would imagine that our general purpose modeling ability grew from needing to model and predict the world and all the yummy food/dangerous animals/warlords in it). The bigger the number of internal models in the engineers mind, the bigger the engineers passion for the project, the more components can be simulated and selected for. Eventually progress is made, and a new subproblem is attacked in the same way, with a shorter path and different input/output to model/regress against. This is very non-magical, which may appall the more intuitive designers among us. It is also a real issue, because it doesn't (or poorly) explains really interesting engineering: e.g. the creation of the Fourier transform, the creation of the expectation-maximization algorithm, all the statistical and mathematical hardware that lends beauty and power to our design lives. When humans create these things, they are at the height of their creative ability, and thus it's probably a bit ridiculous to propose having a computer program do the same. That does not prevent me from poking at the mystery here, though: perhaps it is something akin to random component assembly (and these must be well known components (highly accurate, fast internal models); most all innovations were done by people exceptionally familiar with their territory), with verification against similarly intimately known data (hence, all things in memory - fast 'iteration cycles'). This is not dissimilar to evolutionary approaches to deriving laws. A Cornell physicist / computer scientist was able to generate natural laws via a calculus-infused GA {842}, and other programs were able to derive Copernicus' laws from planetary data. Most interesting scientific formulae are short, which makes them accessible to GAs (and also aesthetically pleasurable, and/or memelike, but hey!). In contrast engineering has many important design patterns that are borrowed by analogy from real-world phenomena, such as the watermark algorithm, sorting, simulated annealing, the MVC framework, object-oriented programming, WIMP interface, verb/noun interface, programming language, even GAs themselves! Douglas Hofstadter has much more to say about analogies, so I defer to him here. Irregardless, as K-- pointed out, without some model for creativity (even one as soulless as the one above), any proposed program-creating program will never come up with anything really new. To use a real-world analogy, at his work the boss is extremely crazy - namely, he mistook a circuit breaker for an elevator (in a one-story factory!). But, this boss also comes up with interminable and enthusiastic ideas, which he throws against the wall of his underlings a few dozen times a day. Usually these ideas are crap, but sometimes they are really good, and they stick. According to K--, the way his mind works is basically opaque and illogical (I've met a few of these myself), yet he performs an essential job in the company - he spontaneously creates new ideas. Without such a boss, he claimed, the creations of a program-creating-program will impoverished. And perhaps hence this should be the first step. Tonight I also learned that at the company (a large medical devices firm) they try to start projects at the most difficult step. That way, projects that are unlikely to succeed are killed as soon as possible. The alternate strategy, which I have previously followed, is to start with the easiest things first, so you get some motivation to continue. Hmm... The quandary to shuffle your internal models over tonight then, dear readers, is this: is creativity actually (or accurately modeled by) random component-combination creation (boss), followed by a selection/rejection (internal auditing, or colleague auditing)? (3)
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Whether Software Engineering Needs to Be Artiï¬cially Intelligent By Herbert Simon, author of __The Sciences of the Artificial__
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Automatic Programming: Myths and Prospects by Charles Rich and Richard C Waters, 1988
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Learning by Playing: Video Games in the Classroom
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Notes & responses to evolutionary psychologists John Toobey and Leda Cosmides' - authors of The Adapted Mind - essay in This Will change Everything
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http://www.gamedev.net/community/forums/topic.asp?topic_id=432583 -- bump, it helped me solve a problem with cgGLGetLatestProfile() failing!! (the class I was using to examine openGL extensions was writing to the openGL supplied string) | |||||||||||||||||||||||||||||||
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http://www.xbdev.net/directx3dx/specialX/Fur/index.php -- for future reference. Simple algorithm that seems to work quite well. Can be done almost entirely in vertex shader... | |||||||||||||||||||||||||||||||
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August 12, 2010 San Francisco (AP) Twitter usage dropped off sharply today with the introduction of twitter2, a instant messaging and subscription service that allows users to send short pithy comments on the inane state of the internet today. According to the inventor, Andrey Ternovskiy, the new service provides 255 characters to play with; the original messaging service was limited to 140. "I don't know why they chose 140 characters", the plucky Russian says, "it means messages span cache lines and/or disc sectors." Regarding this critical insight, Mr.Ternovskiy will offer another service, Twit.com, with 128 characters "for those who really have little to say, yet still want attention." Image of Mr.Ternovskiy with his home. Courtesy of S.Dionysian. | |||||||||||||||||||||||||||||||
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(I'm posting this here as it's easier than putting a image & text in subversion) I'm building a wireless headstage for neural recording. Hence, it has sensitive, high-gain amplifiers (RHA2116) pretty close to a wireless transmitter + serial lines. The transmitter operates intermittently to save power, only sending samples from one continuous channel + threshold crossings for all the other channels. 27 byte-wide samples + channel identifier + 4 bytes threshold crossing are sent in one radio packet; as the radio takes some 130us to start up the PLL, 8 of these packets are chunked together into one frame; one frame is transmitted every 144hz (actually, 1e6/(32*27*8)Hz. At the conclusion of each frame, the continuous channel to be transmitted is incremented. It seems that radio transmission is interfering with the input amplfifiers, as the beginning samples from a frame are corrupted - this is when the previous frame is going out over the air. It could also be noise from the SPI lines, which run under and close to the amplifiers. This may also not be a problem in vivo - it could only be an issue when the input to the amplifiers are floating. Above, a plot of the raw data coming off the headstage radio. Red trace indicates the channel currently being transmitted; blue are the samples. Note that some chanels do not have the artifact - I presume this is because their input is grounded. This will be very tricky to debug, as if we turn off the radio, we'll get no data. Checking if it is a SPI problem is possible by writing the bus at a specified time. Tested with radio PA disabled, it is definitely the SPI bus - routing problem! Stupid. | |||||||||||||||||||||||||||||||
{838} |
ref: -0
tags: meta learning Artificial intelligence competent evolutionary programming Moshe Looks MOSES
date: 08-07-2010 16:30 gmt
revision:6
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Jacques Pitrat seems to have many of the same ideas that I've had (only better, and he's implemented them!)-- A Step toward and Artificial Scientist
Artificial beings - his book. | |||||||||||||||||||||||||||||||
{817} | |||||||||||||||||||||||||||||||
My letter to a friend regarding images/817_1.pdf The free-energy principle: a unified brain theory? PMID-20068583 -- like all critics, i feel the world will benefit from my criticism ;-) Hey , I did read that paper on the plane, and wrote down some comments, but haven't had a chance to actually send them until now. err..anyway.. might as well send them since I did bother writing stuff down: I thought the paper was interesting, but rather specious, especially the way the author makes 'surprise' something to be minimized. This is blatantly false! Humans and other mammals (at least) like being surprised (in the normal meaning of the word). He says things like: "This is where free energy comes in: free energy is an upper bound on surprise, which means that if agents minimize free energy, they implicity minimize surprise -- a huge logical jump, and not one that I'm willing to accept. I feel like this author is trying to capitalize on some recent developments, like variational bayes and ensemble learning, without fully understanding them or having the mathematical chops (like Hayen) to flesh it out. So far as I understand, large theories (as this proposes to be) are useful in that they permit derivation of particular update equations; Variational Bayes for example takes the Kullbeck-Leibler divergence & a factorization of the posterior to create EM update equations. So, even if the free energy idea is valid, the author uses it at such a level to make no useful, mathy predictions. One area where I agree with him is that the nervous system create a model of the internal world, for the purpose of prediction. Yes, maybe this allows 'surprise' to be minimized. But animals minimize surprise not because of free energy, but rather for the much more quotidian reason that surprise can be dangerous. Finally, i wholly reject the idea that value and surprise can be equated or even similar. They seem orthogonal to me! Value is assigned to things that help an animal survive and multiply, surprise is things it's nervous system does not expect. All these things make sense when cast against the theories of evolurion and selection. Perhaps, perhaps selection is a consequence of decreasing free energy - this intuitively and somewhat amorphously/mystically makes sense (the aggregate consequence of life on earth is somehow order, harmony and other 'goodstuff' (but this is an anthropocentric view)) - but if so the author should be able to make more coherent / mathematical prediction of observed phenomena. Eg. why animals locally violate the second law of thermodynamics. Despite my critique, thanks for sending the article, made me think. Maybe you don't want to read it now and I saved you some time ;-) | |||||||||||||||||||||||||||||||
{807} |
ref: -0
tags: reynolds number microorganisms engineering math fluid mechanics
date: 01-25-2010 19:17 gmt
revision:0
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http://jilawww.colorado.edu/perkinsgroup/Purcell_life_at_low_reynolds_number.pdf - great! Never thought about this before.
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{805} | |||||||||||||||||||||||||||||||
http://silentlistening.wordpress.com/2008/05/09/dispersion-of-sound-waves-in-ice-sheets/ -- amazing! | |||||||||||||||||||||||||||||||
{796} | |||||||||||||||||||||||||||||||
An interesting field in ML is nonlinear dimensionality reduction - data may appear to be in a high-dimensional space, but mostly lies along a nonlinear lower-dimensional subspace or manifold. (Linear subspaces are easily discovered with PCA or SVD(*)). Dimensionality reduction projects high-dimensional data into a low-dimensional space with minimum information loss -> maximal reconstruction accuracy; nonlinear dim reduction does this (surprise!) using nonlinear mappings. These techniques set out to find the manifold(s):
(*) SVD maps into 'concept space', an interesting interpretation as per Leskovec's lecture presentation. | |||||||||||||||||||||||||||||||
{795} |
ref: work-0
tags: machine learning reinforcement genetic algorithms
date: 10-26-2009 04:49 gmt
revision:1
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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. | |||||||||||||||||||||||||||||||
{780} | |||||||||||||||||||||||||||||||
A Self-learning Evolutionary Chess Program
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{793} | |||||||||||||||||||||||||||||||
Andrew Ng's notes on learning theory
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{792} | |||||||||||||||||||||||||||||||
http://www.cs.cmu.edu/~wcohen/slipper/
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{790} | |||||||||||||||||||||||||||||||
http://www.carolinamtnclub.com/%5CHiking%5Cgoogle%5C511.htm awesome place! but watch out for the cows! | |||||||||||||||||||||||||||||||
{783} | |||||||||||||||||||||||||||||||
PMID-19435684[0] A 128-channel 6 mW wireless neural recording IC with spike feature extraction and UWB transmitter.
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{787} | |||||||||||||||||||||||||||||||
My theory on the Flynn effect - human intelligence IS increasing, and this is NOT stopping. Look at it from a ML perspective: there is more free time to get data, the data (and world) has almost unlimited complexity, the data is much higher quality and much easier to get (the vast internet & world!(travel)), there is (hopefully) more fuel to process that data (food!). Therefore, we are getting more complex, sophisticated, and intelligent. Also, the idea that less-intelligent people having more kids will somehow 'dilute' our genetic IQ is bullshit - intelligence is mostly a product of environment and education, and is tailored to the tasks we need to do; it is not (or only very weakly, except at the extremes) tied to the wetware. Besides, things are changing far too fast for genetics to follow. Regarding this social media, like facebook and others, you could posit that social intelligence is increasing, along similar arguments to above: social data is seemingly more prevalent, more available, and people spend more time examining it. Yet this feels to be a weaker argument, as people have always been socializing, talking, etc., and I'm not sure if any of these social media have really increased it. Irregardless, people enjoy it - that's the important part. My utopia for today :-) | |||||||||||||||||||||||||||||||
{774} |
ref: work-0
tags: functional programming compilation ocaml
date: 08-24-2009 14:33 gmt
revision:0
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The implementation of functional programming languages - book! | |||||||||||||||||||||||||||||||
{772} |
ref: -0
tags: xmos microcontroller microporcessor threading
date: 08-11-2009 16:15 gmt
revision:0
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{765} | |||||||||||||||||||||||||||||||
shows that I'd like to watch:
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{764} |
ref: work-0
tags: ocaml mysql programming functional
date: 07-03-2009 19:16 gmt
revision:2
[1] [0] [head]
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Foe my work I store a lot of analyzed data in SQL databases. In one of these, I have stored the anatomical target that the data was recorded from - namely, STN or VIM thalamus. After updating the analysis programs, I needed to copy the anatomical target data over to the new SQL tables. Where perl may have been my previous go-to language for this task, I've had enuogh of its strange quiks, hence decided to try it in Ruby (worked, but was not so elegant, as I don't actually know Ruby!) and then Ocaml. ocaml #use "topfind" #require "mysql" (* this function takes a query and a function that converts entries in a row to Ocaml tuples *) let read_table db query rowfunc = let r = Mysql.exec db query in let col = Mysql.column r in let rec loop = function | None -> [] | Some x -> rowfunc col x :: loop (Mysql.fetch r) in loop (Mysql.fetch r) ;; let _ = let db = Mysql.quick_connect ~host:"crispy" ~database:"turner" ~password:"" ~user:"" () in let nn = Mysql.not_null in (* this function builds a table of files (recording sessions) from a given target, then uses the mysql UPDATE command to propagate to the new SQL database. *) let propagate targ = let t = read_table db ("SELECT file, COUNT(file) FROM `xcor2` WHERE target='"^targ^"' GROUP BY file") (fun col row -> ( nn Mysql.str2ml (col ~key:"file" ~row), nn Mysql.int2ml (col ~key:"COUNT(file)" ~row) ) ) in List.iter (fun (fname,_) -> let query = "UPDATE `xcor3` SET `target`='"^targ^ "' WHERE STRCMP(`file`,'"^fname^"')=0" in print_endline query ; ignore( Mysql.exec db query ) ) t ; in propagate "STN" ; propagate "VIM" ; propagate "CTX" ; Mysql.disconnect db ;; Interacting with MySQL is quite easy with Ocaml - though the type system adds a certain overhead, it's not too bad. | |||||||||||||||||||||||||||||||
{762} |
ref: work-0
tags: covariance matrix adaptation learning evolution continuous function normal gaussian statistics
date: 06-30-2009 15:07 gmt
revision:0
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http://www.lri.fr/~hansen/cmatutorial.pdf
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{761} | |||||||||||||||||||||||||||||||
http://www.nytimes.com/2009/05/01/opinion/01brooks.html?_r=1 -- the 'modern view' of genius. Makes sense to me.
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{754} |
ref: Gilbert-2009.03
tags: human prediction estimation social situation neighbor advice affective forecasting
date: 06-10-2009 15:13 gmt
revision:2
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PMID-19299622[0] The Surprising Power of Neighborly Advice.
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{666} | |||||||||||||||||||||||||||||||
PMID-15286181[0] Providing explicit information disrupts implicit motor learning after basal ganglia stroke.
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{742} | |||||||||||||||||||||||||||||||
PMID-17873433[0] A single-chip signal processing and telemetry engine for an implantable 96-channel neural data acquisition system.
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{735} |
ref: -0
tags: processing javascript vector graphics web
date: 05-03-2009 18:20 gmt
revision:0
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http://www.mattryall.net/blog/2008/11/wiki-visualisations-with-javascript -- way cool!! | |||||||||||||||||||||||||||||||
{730} |
ref: -0
tags: recroding biopotential MOS-bipolar pseudoresistor
date: 04-15-2009 22:03 gmt
revision:0
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Linear transconductor with rail-to-rail input swing for very large time constanct applications
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{664} | |||||||||||||||||||||||||||||||
PMID-17946962[0] A reconfigurable neural signal processor (NSP) for brain machine interfaces.
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{364} | |||||||||||||||||||||||||||||||
PMID-17946450[0] An Autonomous, broadband, multi-channel neural recording system for freely behaving primates
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{715} |
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]
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PMID-18846203[0] A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback
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{722} |
ref: notes-0
tags: programming excellence norvig 10 years
date: 04-07-2009 20:26 gmt
revision:0
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Teach yourself programming in 10 years
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{720} | |||||||||||||||||||||||||||||||
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.
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{706} | |||||||||||||||||||||||||||||||
PMID-8987766[0] Functional Stages in the Formation of Human Long-Term Motor Memory
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{712} | |||||||||||||||||||||||||||||||
PMID-19245368[0] The influence of learning on sleep slow oscillations and associated spindles and ripples in humans and rats
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{685} |
ref: BrashersKrug-1996.07
tags: motor learning sleep offline consolidation Bizzi Shadmehr
date: 03-24-2009 15:39 gmt
revision:1
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PMID-8717039[0] Consolidation in human motor memory.
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{701} | |||||||||||||||||||||||||||||||
PMID-18836440[0] Pharmacological REM sleep suppression paradoxically improves rather than impairs skill memory
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{707} | |||||||||||||||||||||||||||||||
PMID-11691982[0] The Role of Sleep in Learning and Memory
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{297} | |||||||||||||||||||||||||||||||
PMID-17182912[0] Skill Representation in the Primary Motor Cortex After Long-Term Practice
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{703} | |||||||||||||||||||||||||||||||
PMID-17167082[0] Elevated sleep spindle density after learning or after retrieval in rats.
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{700} | |||||||||||||||||||||||||||||||
PMID-11691983[0] Sleep, Learning, and Dreams: Off-line Memory Reprocessing
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{693} | |||||||||||||||||||||||||||||||
PMID-16794848[9] Bilateral basal ganglia activation associated with sensorimotor adaptation.
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{695} | |||||||||||||||||||||||||||||||
Alopex: A Correlation-Based Learning Algorithm for Feed-Forward and Recurrent Neural Networks (1994)
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{694} |
ref: Diedrichsen-2005.1
tags: Shadmehr error learning basal ganglia cerebellum motor cortex
date: 03-09-2009 19:26 gmt
revision:0
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PMID-16251440[0] Neural correlates of reach errors.
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{692} | |||||||||||||||||||||||||||||||
PMID-17189946[0] Cortico-hippocampal interaction during up-down states and memory consolidation.
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{680} | |||||||||||||||||||||||||||||||
PMID-17406665[0] Daytime naps, motor memory consolidation and regionally specific sleep spindles.
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{683} | |||||||||||||||||||||||||||||||
PMID-14983183[0] Off-line replay maintains declarative memories in a model of hippocampal-neocortical interactions
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{689} | |||||||||||||||||||||||||||||||
PMID-18958234 Endocannabinoid Signaling is Critical for Habit Formation.
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{686} | |||||||||||||||||||||||||||||||
PMID-17855611 Motor Force Field Learning Influences Visual Processing of Target Motion
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PMID-18274267[0] Fast sleep spindle (13-15 hz) activity correlates with sleep-dependent improvement in visuomotor performance.
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{672} | |||||||||||||||||||||||||||||||
PMID-18714787[0] Motor sequence learning increases sleep spindles and fast frequencies in post-training sleep.
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{671} | |||||||||||||||||||||||||||||||
PMID-18951924[0] Consciousness and the consolidation of motor learning
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{651} | |||||||||||||||||||||||||||||||
PMID-18482830[0] Reinforcement learning of motor skills with policy gradients
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{676} | |||||||||||||||||||||||||||||||
PMID-18578851 Overconfidence in an objective anticipatory motor task.
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{675} | |||||||||||||||||||||||||||||||
PMID-18808769 Modeling the organization of the basal ganglia.
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{674} |
ref: notes-0
tags: Barto Hierarchal Reinforcement Learning
date: 02-17-2009 05:38 gmt
revision:1
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Recent Advancements in Hierarchal Reinforcement Learning
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{673} |
ref: Vasilaki-2009.02
tags: associative learning prefrontal cortex model hebbian
date: 02-17-2009 03:37 gmt
revision:2
[1] [0] [head]
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PMID-19153762 Learning flexible sensori-motor mappings in a complex network.
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{669} | |||||||||||||||||||||||||||||||
PMID-19191602 A New Hypothesis for Sleep: Tuning for Criticality.
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{668} |
ref: notes-0
tags: triangulation kicadocaml
date: 02-04-2009 21:40 gmt
revision:7
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PCB copper zones using triangle meshes Abstract: Many tasks in computer-assisted design involve the removal of polygons from other polygons. Particularly, this problem is found when filling a region of a printed circuit board (PCB) with a polygonal zone or 'pour' of copper. This zone is attached to a net, perhaps ground, and hence other tracks, vias, and segments of copper not on the same net but within its region must be avoided by a clearance distance. This clearance can be observed by subtraction of expanded polygons from the original zone's outline polygon, as is done in two open-source PCB design softwares, Kicad and gEDA. Here we present a fast and scalable algorithm that works with triangles instead of polygons. The algorithm is able to mesh, add edges, and remove conflicting triangles within a few seconds for problems involving 10,000 points. Introduction: I have contributed, infrequently, to the open-source electronic design automation (EDA) suite Kicad for the past year or so. November/December of 2007 I added duplicated hierarchal support to Kicad's schematic editor, eeschema, which allows, like many commercial packages, duplicate instances of sub-schematics. This feature is used when a segment of circuitry is duplicated multiple times in a design, perhaps when there are multiple identical channels, e.g. in an audio mixer. However pcbnew (the layout editor in Kicad) is unaware of the duplication, hence for each sub-schematic the layout had to be duplicated. This involved a lot of work for the 8-channel microstimulator board that I was working on at the time, so I decided to implement a small application to help layout an array of duplicated circuitry. Ocaml was chosen to implement the software, as I wanted to learn the language. In the course of working on PCBs, learning Ocaml, and basically scratching a series of itches, the software, tentatively named "Kicadocaml", has become progressively more feature-rich, useful, and tested. It has ratsnest, DRC online and offline checking, push routing, schematic hierarchy comprehension (of course), connectivity testing, bill-of-materials generation, and a responsive OpenGL-based GUI. In my last board, pcbnew failed to fill all the zones; I'm not sure why. I tried to fix the bug, but got lazy/overwhelmed after a while, and decided to just write a zone-filling algorithm from scratch myself (to scratch the itch, so to speak). Sure it's reinventing the wheel, but reinventing is fun. In the interest of documenting the algorithm a bit for posterity, the algorithm is described below. Algorithm: A list is made of all points and segments that may be involved in the zone-fill. This includes, of course, the edges of the zone, as well as the outline of any track/via/pad cutout within the zone (and not of the same net number), expanded to allow for zone clearance and zone-edge stroking. The list of points also must include any intersections between segments. For efficiency, the lists of points and segments are culled by checking each polygon to be subtracted to make sure that at least one of it's points is within the zone polygon; this is done via the standard inside/outside polygon test. The list of points is then incrementally inserted into a linked triangle mesh via a very simple, very effective method of triangle splitting and edge-flipping. Linked triangle mesh means that each triangle stores a index (or pointer) to the triangle off each of its three edges. This is to facilitate the insertion of points: to find the triangle that a point is in, you walk over the linked mesh, crossing the edge between triangles that intersects a ray from the center of the present triangle to the target point. (Given the ordering of points within the list, this can be nearly a constant-time operation). See below.
Once a triangle is found, it is split into three triangles by the addition of the point. Then, each pair of triangles, one new and one old (bordering the triangle that was split) is checked to see if flipping the interior segment would increase the smallest angle. Remarkably, this reliably takes care of edge insertion - no specialized edge insertion routine was required (however, loops in the find triangle algorithm (figure 1) must be eliminated for a triangle to be found when a point is on an edge). I decided to simply maximize the minimum angle in each triangle, rather than observe the Delaunay criteria which doesn't matter for this application.
This algorithm only deals with finding containing triangles and inserting points; hence, it must be seeded with at least one triangle which will contain all others. I chose to use two triangles defined by a slightly-enlarged bounding box of all points to be inserted. The algorithm does not insure that all polygon segments are in the list of edges of a mesh; hence, after all points are inserted, every edge is checked to make sure if it is in the mesh -- see figure 3.
Once all points and all edges from the original list are in the mesh, then each triangle may be tested to see if it should be kept or removed. In kicadocaml this is done with DRC (design rule check) testing.
Afterword: The algorithm runs well; it takes ~ 2 seconds to mesh, edge check, and filter 10,000 points on my Core2 2.4Ghz desktop computer. Though it was written in a higher-level language (about 600 lines of Ocaml), I do not think that it would be hard to port to C++ for inclusion in other PCB layout packages. Great effort was not necessarily put into the design of the algorithm, but rather the numerical stability of it's sub-components, such as the triangle inside-outside check (computed with the cross product), and the segment intersection test. For these, please see the source, or {661}. | |||||||||||||||||||||||||||||||
{661} |
ref: -0
tags: computational geometry triangulation ocaml kicadocaml zone fill edge
date: 01-26-2009 01:47 gmt
revision:3
[2] [1] [0] [head]
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I have been working hard to add zone support to kicadocaml since the implementation in kicad's PCBnew is somewhat borken (at least for my boards). It is not a very easy task! Roughly, the task is this: given a zone of copper pour, perhaps attached to the ground net, and a series of tracks, vias, and pads also on that layer of the PCB but not on the same net, form cutouts in the zone so that there is an even spacing between the tracks/vias and zone. Currently I'm attacking the problem using triangles (not polygons like the other PCB softwares). I chose triangles since I'm using OpenGL to display the PCB, and triangles are a very native mode of drawing in OpenGL. Points are added to the triangle mesh with an incremental algorithm, where the triangles are stored as a linked-mesh : each triangle has a pointer (index#) to the triangle off edge ab,bc,ca. This allows finding the containing triangle when inserting a point a matter of jumping between triangles; since many of the points to be inserted are close to eachother, this is a relatively efficient algorithm. Once the triangle containing a point to be inserted is found, the triangle is split into three, the pointers are updated appropriately, and each triangle is tested to see if flipping with it's pair would result in a net larger smallest interior angle between the two. (This is not the same as Delaunay's criteria, but it is simpler, and it produces equally beautiful pictures.) The problem is when two triangles are allowed to overlap or a gap is allowed - this makes the search algorithm die or get into a loop, and is a major major problem of the approach. In Guibas and Stolfi's paper, "Primitives for the manipulation of general subdivisions and the computation of Voronoi diagrams", they use an edge data structure, rather than a triangle data structure, which I suppose avoids this problem. I was lazy when starting this project, and chose the more obvious triangle-centric way of storing the data. The insertion of points is actually not so hard; the big problem is making sure the edges in the original list of polygons are represented in the list of edges in the triangle mesh. Otherwise, triangles will span edges, which will result in DRC violations (e.g.g copper too close to vias). My inefficient way of doing this is to calculate, for all triangles, their intersections with the polygon segments, then adding this to the mesh until all segments are represented in the list. This process, too, is prone to numerical instability. Perhaps the solution is to move back to an edge-centric data representation, so that certain edges can be 'pinned' or frozen, and hence they are guaranteed to be in the triangle mesh's edge list. I don't know; need to think about this more. Update: I got most of it working; at least the triangulation & making sure the edges are in the triangle mesh are working. Mostly there were issues with numerical precision with narrow / small triangles; I rewrote the inside triangle function to use the cross product, which helped (this seems like the simplest way, and it avoids divisions!): ocaml let insidetri a b c d = cross (sub b a) (sub d a) > 0.0 && cross (sub c b) (sub d b) > 0.0 && cross (sub a c) (sub d c) > 0.0 ;; as well as the segment-segment intersection algorithm: ocaml let intersect a b c d = (* see if two line segments intersect *) (* return the point of intersection too *) let ab = sub b a in (* a prime is the origin *) let bp = length ab in let xx = norm ab in let yy = (-1.) *. (snd xx) , (fst xx) in let project e = (dot (sub e a) xx) , (dot (sub e a) yy) in let cp = project c in let dp = project d in let cd = sub dp cp in let m = (fst cd) /. (snd cd) in let o = (fst cp) -. m *. (snd cp) in let e = add (scl ab (o /. bp)) a in (* cp and dp must span the x-axis *) if ((snd cp) <= 0. && (snd dp) >= 0.) || ((snd cp) >= 0. && (snd dp) <= 0.) then ( if o >= 0. && o <= bp then ( true, e ) else ( false, e ) ) else ( false, e ) ;; Everything was very sensitive to ">" vs. ">=" -- all must be correct. All triangles must be CCW, too, for the inside algorithm to work - this requires that points to be inserted close to a triangle edge must be snapped to that edge to avoid any possible CW triangles. (Determining if a triangle is CW or CCW is as simple as measuring the sign of the smallest cross product between two segments). I tried, for a day or so, to include a specialized function to insert points along a triangle's edge, but that turned out not to matter; the normal flipping routine works fine. I also tried inserting auxiliary points to try to break up very small triangles, but that really didn't affect the stability of the algorithm much. It is either correct, or it is not, and my large board was a good test suite. I have, however, seeded the triangularization with a grid of (up to) 20x20 points (this depends on the aspect ratio of the region to be filled - the points are equally spaced in x and y). This adds (max) 800 triangles, but it makes the algorithm more stable - fewer very narrow triangles - and we are working with sets of 10,000 triangles anyway for the large zones of copper. Some corrections remain to be done regarding removing triangles based on DRC violation and using the linked-mesh of triangles when calculating edge-triangle edge intersection, but that should be relatively minor. Now I have to figure out how to store it in Kicad's ".brd" file format. Kicad uses "Kbool" library for intersection polygons - much faster than my triangle methods (well, it's in C not ocaml) - and generates concave polygons not triangles. Would prefer to do this so that I don't have to re-implement gerber export. (Of course, look at how much I have re-implemented! This was originally a project just to learn ocaml - Well, gotta have some fun :-) | |||||||||||||||||||||||||||||||
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PMID-11411161[0] The anterior cingulate cortex. The evolution of an interface between emotion and cognition
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PMID-12371511[0] Dopamine: generalization and bonuses
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{652} |
ref: notes-0
tags: policy gradient reinforcement learning aibo walk optimization
date: 12-09-2008 17:46 gmt
revision:0
[head]
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Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion
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{178} |
ref: Churchland-2006.12
tags: motor_noise CNS Churchland execution variance motor_planning 2006
date: 12-08-2008 22:50 gmt
revision:2
[1] [0] [head]
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PMID-17178410[0] A central source of movement variability.
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Section 3 - Video tracking and host computer controlWith the microcontroller done, we then moved to controlling it via a video-tracking computer. At this point, we had created a simple program for testing out parallel port control of the light's three axes using the keyboard (tilt, pan, and shutter). This program was split into two files, a main, and a set of subroutines that could then be called and compiled into the full video tracking program. It uses libparapin to abstract interaction with the parallel port in userspace.First, the main loop, which is very simple: #include "parallelout.h" #include <stdio.h> #include <stdlib.h> #include <unistd.h> char g_main_loop ; int main(int argc, char *argv[]) { g_main_loop = 1; char c; parallel_setup(); while(1){ c = fgetc(stdin); interpret_cmd(c); } } Second, the parallel port controller. This uses a thread and circular queue to provide asynchronous, non-blocking control of the communications channel. Non-blocking is critical, as the program waits a small period between low-high transition of the interrupt pin (pin 4) for the MSP430 to read the status of the three lines. #include <stdio.h> #include <stdlib.h> #include <unistd.h> #include <parapin.h> #include <pthread.h> #include "parallelout.h" char g_q[1024]; //queue for the commands. int g_q_rptr; //where to read the next command from. int g_q_wptr; //where to put the next command double g_velPan; double g_velTilt; void stepstep(void){ int i = 0; for(i=0; i<20000; i++){ set_pin(LP_PIN[4]); clear_pin(LP_PIN[4]); set_pin(LP_PIN[5]); clear_pin(LP_PIN[5]); set_pin(LP_PIN[5]); clear_pin(LP_PIN[5]); } } void velstep(int n){ //printf("velstep %d\n", n); clear_pin(LP_PIN[4]); if(n&0x1) set_pin(LP_PIN[2]) ; else clear_pin(LP_PIN[2]); if(n&0x2) set_pin(LP_PIN[3]) ; else clear_pin(LP_PIN[3]); set_pin(LP_PIN[4]); //leave it up, so the msp430 knows it is a velocity command. } void openShutter(){ printf("opening shutter\n"); clear_pin(LP_PIN[4]); set_pin(LP_PIN[2]); set_pin(LP_PIN[3]); set_pin(LP_PIN[4]); clear_pin(LP_PIN[4]); //clear the trigger to indicate a shutter command. } void closeShutter(){ printf("closing shutter\n"); clear_pin(LP_PIN[4]); clear_pin(LP_PIN[2]); clear_pin(LP_PIN[3]); set_pin(LP_PIN[4]); clear_pin(LP_PIN[4]); //clear the trigger to indicate a shutter command. } void smallShutter(){ printf("small shutter\n"); clear_pin(LP_PIN[4]); clear_pin(LP_PIN[2]); set_pin(LP_PIN[3]); set_pin(LP_PIN[4]); clear_pin(LP_PIN[4]); //clear the trigger to indicate a shutter command. } void stopMirror(){ printf("stop mirror\n"); clear_pin(LP_PIN[4]); set_pin(LP_PIN[2]); clear_pin(LP_PIN[3]); set_pin(LP_PIN[4]); clear_pin(LP_PIN[4]); //clear the trigger to indicate a shutter command. } void parallel_setup(){ if (pin_init_user(LPT1) < 0) exit(0); pin_output_mode(LP_DATA_PINS | LP_SWITCHABLE_PINS); clear_pin(LP_PIN[2]); clear_pin(LP_PIN[3]); clear_pin(LP_PIN[4]); pthread_t thread1; //start the queue-servicing thread. pthread_create ( &thread1, NULL, pq_thread, NULL ); } void interpret_cmd(char cmd){ //these codes don't make much sense unless you are //controlling from a keyboard. switch(cmd){ case 'w': velstep(0); g_velPan-=1.0; break; //pan to right (looking at but of light) case 'a': velstep(1); g_velTilt+=1.0; break; //tilt toward. case 's': velstep(2); g_velPan+=1.0; break; //pan to left case 'd': velstep(3); g_velTilt-=1.0; break; //tilt away case 'o': openShutter(); break; case 'c': closeShutter(); break; case 'x': smallShutter(); break; case ' ': stopMirror(); g_velPan=0; g_velTilt=0; break; } } void usleep(int us){ timespec ts; ts.tv_sec = 0; ts.tv_nsec = us * 1000; nanosleep(&ts, NULL); } extern int g_main_loop ; void* pq_thread(void* a){ while(g_main_loop){ if(g_q_wptr > g_q_rptr){ char cmd = g_q[g_q_rptr % sizeof(g_q)]; g_q_rptr++; interpret_cmd(cmd); } usleep( 200 ); // run at max 500hz update. // the msp430 takes about 125us to service the parallel port irq. } return (void*)0; } void enqueue(char cmd){ //this should be sufficiently atomic so there is no thread contention. g_q[g_q_wptr % sizeof(g_q)] = cmd; g_q_wptr++; } Then, we worked on the video tracking program. I will omit the some of the noncritical sections involving the firewire (ieee1394), Xv (video display) and X11 (window manager) calls, as the whole program is long, ~1000 lines. Below is 'main' -- see the comments for a detailed description. int main(int arc, char *argv[]){ int i; double t1, t2, t3, t4; t1 = t2 = t3 = t4 = 0.0; signal(SIGINT, cleanup); //trap cntrl c signal(SIGPIPE, cleanup); //turn off output buffering for ttcp! //setvbuf(stdout,(char*)NULL,_IONBF,0); //init buffers for old tracking... for(int i=0; i<4; i++){ g_buffer[i] = (short*)malloc(640*480*sizeof(short)); } g_averagefb = (int*)malloc(640*480*sizeof(int)); g_velfb = (int*)malloc(640*480*sizeof(int)); g_lastfb = (unsigned char*)malloc(640*480); g_trackedfb = (unsigned char*)malloc(640*480); for(i=0; i < 640*480; i++){ g_averagefb[i] = 0; } //Step -2: set up threads (the display runs on a seperate thread // to keep from blocking the iscochronous recieve channel // and hence causing the frame rate to drop. pthread_mutex_init(&g_dispthread_mutex, NULL); pthread_cond_init(&g_dispthread_cond, NULL); //STEP -1: init the parallel port for control of mirror (this also starts that thread) parallel_setup(); //Step 0: small shutter so we can track the light easily. smallShutter(); //Step 0.5: move the mirror to the close left (from but of light) for calibration //for reference (from the viewpoint of the cord end of the light): // pan left : s // pan right: w // tilt toward: a // tilt away: s // small shutter: x // open shutter: o // closed shutter: c // stop mirrors : <space> for(i=0; i<10; i++){ enqueue('s'); //to the left if you are looking at the but of the light. enqueue('a'); //the tilt axis has far fewer steps for full range than enqueue('s'); // the pan axis hence requires a much higher velocity - enqueue('s'); // so enqueue more 's'. enqueue('s'); enqueue('s'); } //Step 1: Open ohci and assign a handle to it. //================================================================================================================== init_cards(); //Step 2: Get the camera nodes and describe them as we find them. //================================================================================================================== init_cams(); //Step 3: Setup Capture //================================================================================================================== setup_cams(); //Step 4: Start sending data //================================================================================================================== start_iso_transmission(); //start the other thread. pthread_t thread1; pthread_attr_t attr; pthread_attr_init(&attr); pthread_create( &thread1, &attr, display_thread, 0 ); //Main event loop while(g_main_loop==true){ for( i=0; i<numCameras; i++){ if(dc1394_dma_single_capture(&camera[i]) != DC1394_SUCCESS){ fprintf(stderr, "dma1394: Failed to capture from cameras\n"); cleanup(0); } } t2=get_time(); for( i=0; i<numCameras; i++){ //display_frames_old(i); display_frames(i); /*if((g_frame%60) < 15){ velstep(0); }else if((g_frame%60) < 45){ velstep(2); }else { velstep(0); } */ if(dc1394_dma_done_with_buffer(&camera[i]) != DC1394_SUCCESS){ fprintf(stderr, "dma1394: Can't release dma bufer\n"); } } if(g_frame % 60 == 0){ printf("frame dt: %f (%f) track time %f (%f)\n", t2-t4, 1/(t2-t4), tracktime, 1/(tracktime)); } //start with the state machine for the calibration -- if(g_frame == CALIB_0){ enqueue(' '); //stop it printf("!!assuming that the mirror reached it's limit!!\n"); for( i=0; i<3; i++){ //now that we have put the mirror into a corner, give it velocity // to move to the center of the FOV so that we may turn on // feedback-based tracking. enqueue('w'); //to the left if you are looking at the but of the light. enqueue('d'); enqueue('w'); //again, pan motor has many more steps/range than tilt enqueue('w'); enqueue('w'); enqueue('w'); enqueue('w'); enqueue('w'); enqueue('w'); } } if(g_frame == CALIB_1){ enqueue(' '); //stop it printf("!!assuming light is centered now!!\n"); } if(g_frame == CALIB_2){ enqueue('x'); } t4 = t2; g_frame++; } cleanup(0); return 0; } Our tracking algorithm periodically opens and closes the shutter on the light. It is impossible to track a target based on brightness or even pattern detection, since the light is so bright it is impossible to image what it hits and what it does not with our cameras of limited dynamic range. (The human eye, of course, has far better dynamic range.) During the period when the light is off, we wait for the camera shutter speed to stabilize, then average the brightest spot over 10 consecutive frames to obtain a target position. Then, the shutter is opened, and visual feedback is used with a simple PD controller to guide the light to the target. When the device is deployed, we will make the update non-periodic and purely contingent on the detection of motion or of decreased solar cell output. See below for the thread that implements this logic, as well as blits the image onto the screen. void* display_thread(void* ptr ){ make_window(); while(g_main_loop){ if(pthread_cond_wait(&g_dispthread_cond, &g_dispthread_mutex) == 0){ pthread_mutex_unlock(&g_dispthread_mutex); double t1 = get_time(); g_first_frame=false; //convert into the XV format (this seems very inefficient to me...) for(unsigned int i=0; i< g_framewidth*g_frameheight; i++){ g_fb[i] = g_trackedfb[i]+ 0x8000; } double c_r, c_c; new_track(0, &tcam[0], g_trackedfb, g_framewidth, g_frameheight, CONTRAST_MIN, SEARCHR, GAUSSDROPOFF, NUMBER_OF_MARKERS, &c_r, &c_c); xv_image=XvCreateImage(display, info[adaptor].base_id, format_disp, (char*)g_fb, g_framewidth, g_frameheight*numCameras); XvPutImage(display, info[adaptor].base_id, window, gc, xv_image, 0, 0, g_framewidth, g_frameheight*numCameras, 0, 0, g_windowwidth, g_windowheight); free(xv_image); //do some dumb control (finally!) // initially, guide the light to the center of the screen. if(g_frame > CALIB_1 && g_frame <= CALIB_2){ g_target_c = 320.0; g_target_r = 240.0; servo_mirror(c_c, c_r); //get it stuck on the center! } int time = g_frame - CALIB_2; // below is the *main loop* for cycling the shutter open/close if(g_frame > CALIB_2){ if(time % 300 < 240){ servo_mirror(c_c, c_r); } if(time % 300 == 240){ enqueue('c'); enqueue(' '); } if(time % 300 >= 260 && time % 300 < 280 ){ g_target_c += c_c; g_target_r += c_r; } if(time % 300 == 280){ enqueue('x'); g_target_c /= 20.0; g_target_r /= 20.0; } } double t2 = get_time(); tracktime = t2 - t1 ; } //normalize_com(NUMBER_OF_MARKERS); XFlush(display); while(XPending(display)>0){ XNextEvent(display,&xev); switch(xev.type){ case ConfigureNotify: g_windowwidth=xev.xconfigure.width; g_windowheight=xev.xconfigure.height; break; case KeyPress: switch(XKeycodeToKeysym(display,xev.xkey.keycode,0)){ case XK_q: case XK_Q: g_main_loop = false; //cleanup(0); break; } break; } } //XPending } if ((void *)window != NULL){ XUnmapWindow(display,window); } fprintf(stderr,"dma1394: Unmapped Window.\n"); if (display != NULL){ XFlush(display); } return (void*) 0; } The PD controller uses very pessimistic values for the coefficients, as we discovered that the timing resolution on out older linux computer is low - about 5ms. This means that if too many velocity step commands are sent to the parallel port thread at one time, it will get backlogged, which will induce a phase-shift between control and actuation of velocity. Hence, the light must move rather slowly, on the order of one velocity step on each axis per frame. Again, below. void servo_mirror(double c_c, double c_r ){ double dc = c_c - g_target_c; //for now assume that we want to stabilize in double dr = c_r - g_target_r; // the center. double vgain = 8.0 ; double pgain = 1.0/80.0; int lim = 1; double c = dc + g_velPan*vgain ; int ccmd = 0; int rcmd = 0; if(c > 0){ for(int i=0; i<c*pgain && i < lim; i++){ enqueue('w'); ccmd --; } } if(c < 0){ for(int i=0; i<c*-1.0*pgain && i < lim; i++){ enqueue('s'); ccmd ++; } } vgain *= 1.5; //tilt mirror moves quicker! double r = dr + g_velTilt*vgain; if(r>0){ for(int i=0; i<r*pgain && i < lim; i++){ enqueue('d'); rcmd--; } } if(r<0){ for(int i=0; i<r*-1.0*pgain && i < lim; i++){ enqueue('a'); rcmd++; } } //this for debugging loop stability problems in matlab. //printf("%f %f %d %f %f %d\n", dc, g_velPan*vgain, ccmd, dr, g_velTilt*vgain, rcmd); //if(dr + g_velTilt*vgain > 0) enqueue('d'); OLD //if(dr + g_velTilt*vgain < 0) enqueue('a'); } Our video tracking algorithm first uses a tree-like algorithm to quickly and robustly search for the brightest region in the scene; we presume, somewhat simplistically, that this will be the target. When the device is put into use with an actual monkey cage, we'll surround the camera with high-intensity infrared LEDs to effectively illuminate a retroreflector placed on the monkey's head. Below is the code which performs this computation. //make a blur matrix //void blur(frame_info* frame, unsigned char * fb, int framewidth, int downsamp, int downsamp_w, int downsamp_h){ void blur(int camno, track_cam* tcam, unsigned char* fb, int framewidth, int downsamp_r, int downsamp_c, int downsamp_w, int downsamp_h){ //initialize contrasts for(int m=0; m<downsamp_r * downsamp_c; m++){ tcam[camno].frame.sum[m]=0; tcam[camno].frame.contr_min[m]=255; tcam[camno].frame.contr_max[m]=0; } for(int k=0; k<downsamp_r; k++){ for(int row=k*downsamp_h; row<k*downsamp_h+downsamp_h; row++){ for(int j=0; j<downsamp_c; j++){ for(int col=j*downsamp_w; col<j*downsamp_w+downsamp_w; col++){ tcam[camno].frame.sum[j+(k*downsamp_c)]+=int(fb[row*framewidth+col]); if(int(fb[row*framewidth+col])>tcam[camno].frame.contr_max[j+(k*downsamp_c)]){ tcam[camno].frame.contr_max[j+(k*downsamp_c)]=int(fb[row*framewidth+col]); //introducing a contrast check. } if(int(fb[row*framewidth+col])<tcam[camno].frame.contr_min[j+(k*downsamp_c)]){ tcam[camno].frame.contr_min[j+(k*downsamp_c)]=int(fb[row*framewidth+col]); //introducing a contrast check } } } } } } //blob_search function //search through the sum matrix and find the brightest sums //void blob_search(frame_info* frame, marker* marker, int num_markers, int contrast_min){ void blob_search(int camno, track_cam* tcam, int num_markers, int contrast_min, int downsamp_r, int downsamp_c){ //frame->num_blobs=0; //innocent until proven guilty for(int i=0; i<num_markers; i++){ int blob_val=0; for(int m=0; m<downsamp_r*downsamp_c; m++){ if(tcam[camno].frame.sum[m]>blob_val && tcam[camno].frame.contr_max[m]-tcam[camno].frame.contr_min[m]>contrast_min){ //has to have a big contrast to be a blob (CONTRAST is user defined macro) blob_val=tcam[camno].frame.sum[m]; //the new max' tcam[camno].marker[i].downsamp_loc=m; //the sum integer (0-255) //frame->num_blobs++; } } tcam[camno].frame.sum[tcam[camno].marker[i].downsamp_loc]=0; //kill the one we just found so we can find the next biggest one. } } //brightest_pix_search function //search through the blobs for the brightest pixel //void brightest_pix_search(unsigned char * fb, frame_info* frame, marker* marker, int num_markers, int framewidth, int downsamp, int downsamp_w, int downsamp_h){ void brightest_pix_search(unsigned char * fb, int camno, track_cam* tcam, int num_markers, int framewidth, int downsamp_r, int downsamp_c, int downsamp_w, int downsamp_h){ //br_pix_info[0] is the row //br_pix_info[1] is the col //br_pix_info[2] is the value for(int i=0; i<num_markers; i++){ tcam[camno].marker[i].br_pix_val=0; //always has to start low for(int row=int(floor(tcam[camno].marker[i].downsamp_loc/downsamp_c))*downsamp_h; row<int(floor(tcam[camno].marker[i].downsamp_loc/downsamp_c))*downsamp_h+downsamp_h; row++){ for(int col=tcam[camno].marker[i].downsamp_loc%downsamp_c*downsamp_w; col<tcam[camno].marker[i].downsamp_loc%downsamp_c*downsamp_w+downsamp_w; col++){ if(int(fb[row*framewidth+col])>tcam[camno].marker[i].br_pix_val){ //if it is greater than the brightest pixel then store its info tcam[camno].marker[i].br_pix_row=row; //save the row tcam[camno].marker[i].br_pix_col=col; //save the column tcam[camno].marker[i].br_pix_val=int(fb[row*framewidth+col]); //save the value } } } } } The blocking (or blobbing) and search algorithm yields the estimated location of the brightest pixel in the image. This is passed to a specialized array-growth region growing algorithm which dynamically expands a region around the suggested brightest pixel to include all pixels that are within a threshold of brightness to the brightest. The region growing algorithm then computes the center of mass from the list of pixel coordinates, which are then passed to the PD and target location routines. void region_grow(unsigned char * src, unsigned short* dest, int w, int h, int b_r, int b_c, double* c_r, double* c_c){ //need to do an expansion from the brightest point. //this is sorta a random-access op - which is bad. unsigned short fill = 0xff00; int n = 0; short r, c; int i, p; unsigned char thresh = 20 ; unsigned char brightest = src[w*b_r + b_c]; g_rows[n] = b_r; g_cols[n] = b_c; n++; int sta = 0; int end = 0; int lim = sizeof(g_rows)/sizeof(int); while(n < lim && n > sta){ //loop through all the new points, adding to the set as we go. end = n; for(i=sta; i < end; i++){ r = g_rows[i]; c = g_cols[i]; r++; //down if(r >= 0 && r < h && c >= 0 && c < w && n < lim){ p = r*w +c; if(brightest - src[p] < thresh){ src[p] = 0; dest[p] = fill; g_rows[n] = r; g_cols[n] = c; n++; } } r -= 2; //up. if(r >= 0 && r < h && c >= 0 && c < w && n < lim){ p = r*w +c; if(brightest - src[p] < thresh){ src[p] = 0; dest[p] = fill; g_rows[n] = r; g_cols[n] = c; n++; } } r++; //center c++; //to the right. if(r >= 0 && r < h && c >= 0 && c < w && n < lim){ p = r*w +c; if(brightest - src[p] < thresh){ src[p] = 0; dest[p] = fill; g_rows[n] = r; g_cols[n] = c; n++; } } c-=2; //to the left. if(r >= 0 && r < h && c >= 0 && c < w && n < lim){ p = r*w +c; if(brightest - src[p] < thresh){ src[p] = 0; dest[p] = fill; g_rows[n] = r; g_cols[n] = c; n++; } } }//end loop over past points. sta = end; } //calculate the center of mass. double cm_r = 0; double cm_c = 0; for(i=0; i<n; i++){ cm_r += g_rows[i]; cm_c += g_cols[i]; } cm_r /= n; cm_c /= n; *c_r = cm_r; *c_c = cm_c; //printf("point: %f %f %d \n", cm_r, cm_c, g_frame++); int cm_r_i, cm_c_i; cm_r_i = (int)cm_r; cm_c_i = (int)cm_c; if(cm_c_i >= 0 && cm_c_i < w && cm_r_i >= 0 && cm_r_i < h) dest[cm_r_i*w + cm_c_i] = 0xffff; } And that is, roughly, the entirety of the video tracking program! (Most of the rest of the code deals with the firewire bus and other less interesting details.) We conclude with a picture of the whole setup in the office. | |||||||||||||||||||||||||||||||
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http://www.linux-mag.com/id/7187 -- has a very interesting and very well applied analogy between programs and laws. I am inclined to believe that they really are not all that different; legalese is structured and convoluted the way it is because it is, in effect, a programming language for laws, hence must be precise and unambiguous. Furthermore, the article is well written and evidences structured and balanced thought (via appropriate references to the real world). And he uses Debian ;-) | |||||||||||||||||||||||||||||||
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hopefully these links don't move..
I like these 'paints', too. Did you show them to me a long time ago? I remember someone showing them to me in the past and I am wondering if you were the one. -- Ana | |||||||||||||||||||||||||||||||
{636} | |||||||||||||||||||||||||||||||
PMID-9448252[0] The acquisition of skilled motor performance: Fast and slow experience-driven changes in primary motor cortex
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{635} | |||||||||||||||||||||||||||||||
“Seeing†through the tongue: cross-modal plasticity in the congenitally blind
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{634} |
ref: RAzsa-2008.01
tags: nAChR nicotinic acetylchoine receptor interneurons backpropagating LTP hippocampus
date: 10-08-2008 17:37 gmt
revision:0
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PMID-18215234[0] Dendritic nicotinic receptors modulate backpropagating action potentials and long-term plasticity of interneurons.
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{631} | |||||||||||||||||||||||||||||||
PMID-16563737[0] The computational neurobiology of learning and reward
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{629} | |||||||||||||||||||||||||||||||
PMID-11257908[0] Multiple Reward Signals in the Brain
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{628} | |||||||||||||||||||||||||||||||
PMID-10731222[0] Reward processing in primate orbitofrontal cortex and basal ganglia
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{627} | |||||||||||||||||||||||||||||||
PMID-9530495[0] Cortical plasticity: from synapses to maps
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{625} | |||||||||||||||||||||||||||||||
PMID-8423485[0] Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys
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{618} | |||||||||||||||||||||||||||||||
PMID-11506661[0] Parallel cortico-basal ganglia mechanisms for acquisition and execution of visuomotor sequences - a computational approach.
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{617} | |||||||||||||||||||||||||||||||
PMID-12015240[0] Central mechanisms of motor skill learning
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{126} | |||||||||||||||||||||||||||||||
PMID-8091209[0] The basal ganglia and adaptive motor control (I couldn't find the pdf for this)
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{613} | |||||||||||||||||||||||||||||||
PMID-12383782[0] Reward, motivation, and reinforcement learning.
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{236} | |||||||||||||||||||||||||||||||
PMID-8985875 Neural information transferred from the putamen to the globus pallidus during learned movement in the monkey.
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{67} | |||||||||||||||||||||||||||||||
PMID-16271465[] The basal ganglia: Learning new tricks and loving it
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{611} | |||||||||||||||||||||||||||||||
PMID-18667540[0] Learning a novel myoelectric-controlled interface task.
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{610} | |||||||||||||||||||||||||||||||
PMID-12705427[0] A SEEG study of ERP in motor and premotor cortices and in the basal ganglia.
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{609} |
ref: -0
tags: differential dynamic programming machine learning
date: 09-24-2008 23:39 gmt
revision:2
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{289} | |||||||||||||||||||||||||||||||
PMID-11395017[0] Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field
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{608} | |||||||||||||||||||||||||||||||
PMID-14511525 Probing changes in neural interaction during adaptation.
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{264} | |||||||||||||||||||||||||||||||
PMID-15588812[0] Tools for the body schema See also PMID-8951846[1] Coding of modified body schema during tool use by macaque postcentral neurones. ____References____ | |||||||||||||||||||||||||||||||
{329} | |||||||||||||||||||||||||||||||
PMID-17234689[0] Volitional control of neural activity: implications for brain-computer interfaces (part of a symposium)
humm.. this paper came out a month ago, and despite the fact that he is much older and more experienced than i, we have arrived at the same conclusions by looking at the same set of data/papers. so: that's good, i guess. ____References____ | |||||||||||||||||||||||||||||||
{596} | |||||||||||||||||||||||||||||||
PMID-11081826 EMG activation patterns during force production in precision grip. III. Synchronisation of single motor units.
Dr. hepp-Raymond himself seems to be a prolific researcher, judging from his pubmed search results. e.g.:
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{586} | |||||||||||||||||||||||||||||||
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 ;) | |||||||||||||||||||||||||||||||
{577} | |||||||||||||||||||||||||||||||
I found this on my computer tucked away into a dusty corner. Such fascinating information should not be left hidden - | |||||||||||||||||||||||||||||||
{227} |
ref: notes-0
tags: expectation maximization EM clustering autosorting
date: 06-16-2008 19:40 gmt
revision:5
[4] [3] [2] [1] [0] [head]
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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/ | |||||||||||||||||||||||||||||||
{572} |
ref: bookmark-0
tags: memory supermemo leraning psychology Hermann Ebbinghaus
date: 05-08-2008 15:25 gmt
revision:0
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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.
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{571} | |||||||||||||||||||||||||||||||
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{375} | |||||||||||||||||||||||||||||||
This is from the CSR reference design for the BlueCore5 chip. They also note that you have to pay attention to the aspect ratio of the vias - with laser drilling, this means that they needed a 63um prepreg between layers 1 and 2 (ground), with start copper thickness of 18um. PTH = plated-through-hole. (refers to a type of via) For 0.8mm BGA, you can loosen the design rules to the following: "
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{552} | |||||||||||||||||||||||||||||||
From the New Yorker, Feb 25:
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{536} | |||||||||||||||||||||||||||||||
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{538} | |||||||||||||||||||||||||||||||
{530} | |||||||||||||||||||||||||||||||
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{514} | |||||||||||||||||||||||||||||||
spectrum options for broadband wireless
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{500} | |||||||||||||||||||||||||||||||
This was written for the plik-l mailing list, Nov 16 2007 I actually had a bit of an argument yesterday with my dentist, no less, about global warming:
Mostly I'd have to agree with the dentist - the oil is going to be burned eventually, because it is just such a cheap source of energy. We are going to have to deal with the consequences. However, for coal - of which we have a far greater supply, and is considerably more dangerous / expensive to obtain - there is good reason to search for alternatives, and putting a tax on oil/natural gas now fund development of alternatives is probably very future-responsible, and will shift the energy climate so we relinquish coal (and maybe some oil) earlier, resulting in less CO2 in the atmosphere. There are infinitely many things more worthy/long-range responsible than the war, but our leaders have not touched on that. Correct me if I'm wrong, but there is little evidence that they even measured the worth of all alternatives, and decided rationally, based on integrating (over time and path probability) best-of-present knowledge of benefits and consequences. Or maybe they decided rationally, but with the worth of alternatives measured *personally*. It is this that truly angers me. Bayes for president 2008! Comments:
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{475} | |||||||||||||||||||||||||||||||
http://www.neuroconnex.com/ -- looks like they have some excellent products, but not sure how to purchase them.
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{471} | |||||||||||||||||||||||||||||||
Little Stupid Details - what they are, and how to avoid them
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{466} | |||||||||||||||||||||||||||||||
What I have learned about licensing & Duke (or really, licensing at universities in general), in no particular order:
conclusions:
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{463} | |||||||||||||||||||||||||||||||
http://www.ntlf.com/html/lib/quotes.htm
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{445} | |||||||||||||||||||||||||||||||
pretty impressive project, especially considering how much time and money they spent ($15 m, 6 man-months to do the verilog (only!)) http://www.hotchips.org/archives/hc16/3_Tue/1_HC16_Sess6_Pres1_bw.pdf | |||||||||||||||||||||||||||||||
{442} | |||||||||||||||||||||||||||||||
http://mirror.mricon.com/french/french.html -- "how i learned french in a year"
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{409} |
ref: bookmark-0
tags: optimization function search matlab linear nonlinear programming
date: 08-09-2007 02:21 gmt
revision:0
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http://www.mat.univie.ac.at/~neum/ very nice collection of links!! | |||||||||||||||||||||||||||||||
{403} |
ref: bookmark-0
tags: blackfin ELF freestanding applications boot
date: 08-01-2007 14:40 gmt
revision:0
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http://www.johanforrer.net/BLACKFIN/index.html very good, very instructive. | |||||||||||||||||||||||||||||||
{386} | |||||||||||||||||||||||||||||||
http://video.google.com/videoplay?docid=-3254488777215293198 need to learn more about this infamous federal reserve! | |||||||||||||||||||||||||||||||
{371} | |||||||||||||||||||||||||||||||
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{360} |
ref: thesis-0
tags: clementine 042607 operant conditioning
date: 04-27-2007 16:45 gmt
revision:3
[2] [1] [0] [head]
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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): | |||||||||||||||||||||||||||||||
{358} |
ref: thesis-0
tags: clementine 042507 operant conditioning
date: 04-25-2007 20:19 gmt
revision:2
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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) | |||||||||||||||||||||||||||||||
{356} |
ref: thesis-0
tags: clementine 042407 operant conditioning
date: 04-25-2007 00:21 gmt
revision:1
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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:
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. movies:
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{355} |
ref: thesis-0
tags: clementine 042307 operant conditioning
date: 04-24-2007 01:37 gmt
revision:2
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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 | |||||||||||||||||||||||||||||||
{71} |
ref: Francis-2005.11
tags: Joe_Francis motor_learning reaching humans delay intertrial interval
date: 04-09-2007 22:48 gmt
revision:1
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PMID-16132970[0] The Influence of the Inter-Reach-Interval on Motor Learning. Previous studies have demonstrated changes in motor memories with the passage of time on the order of hours. We sought to further this work by determining the influence that time on the order of seconds has on motor learning by changing the duration between successive reaches (inter-reach-interval IRI). Human subjects made reaching movements to visual targets while holding onto a robotic manipulandum that presented a viscous curl field. We tested four experimental groups that differed with respect to the IRI (0.5, 5, 10 or 20 sec). The 0.5 sec IRI group performed significantly worse with respect to a learning index than the other groups over the first set of 192 reaches. Each group demonstrated significant learning during the first set. There was no significant difference with respect to the learning index between the 5, 10 or 20 sec IRI groups. During the second and third set of 192 reaches the 0.5 sec IRI group's performance became indistinguishable from the other groups indicating that fatigue did not cause the initial poor performance and that with continued training the initial deficit in performance could be overcome. ____References____ | |||||||||||||||||||||||||||||||
{129} | |||||||||||||||||||||||||||||||
PMID-10607637[0] Internal models for motor control and trajectory planning
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{106} | |||||||||||||||||||||||||||||||
PMID-15208695[0] PDF HTML summary Optimal feedback control and the neural basis of volitional motor control by Stephen S. Scott ____References____ | |||||||||||||||||||||||||||||||
{32} | |||||||||||||||||||||||||||||||
http://www.ixo.de/info/usb_jtag/ open source USB Jtag adapter, works with dragon (I think!) | |||||||||||||||||||||||||||||||
{278} | |||||||||||||||||||||||||||||||
PMID-17143147[0] Decoding movement intent from human premotor cortex neurons for neural prosthetic applications
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{337} | |||||||||||||||||||||||||||||||
PMID-2723767[0] A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimensional reaching task.
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{343} | |||||||||||||||||||||||||||||||
PMID-3928831[0] Cerebellar nuclear cell activity during antagonist cocontraction and reciprocal inhibition of forearm muscles. by kalaska concering the interpositus dentate & isometric task.
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{339} | |||||||||||||||||||||||||||||||
PMID-8817266[0] On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional static isometric force.
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{333} |
ref: BrashersKrug-1996.07
tags: consolidation motor learning Shadmher Bizzi
date: 04-09-2007 14:35 gmt
revision:2
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PMID-8717039[0] Consolidation in human motor memory
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{332} | |||||||||||||||||||||||||||||||
PMID-17234696[0] Brain-computer interfaces: communication and restoration of movement in paralysis
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{328} | |||||||||||||||||||||||||||||||
PMID-4219745[0] Relation of basal ganglia, cerebellum, and motor cortex units to ramp and ballistic limb movements.
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{325} | |||||||||||||||||||||||||||||||
PMID-8768391[0] Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey
PMID-16339894[1] Neurons of the cerebral cortex exhibit precise interspike timing in correspondence to behavior.
PMID-7770778[2] Reliability of spike timing in neocortical neurons.
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{321} | |||||||||||||||||||||||||||||||
http://www-evasion.imag.fr/Publications/2007/WBKMCL07/surveyHair.pdf
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{258} | |||||||||||||||||||||||||||||||
PMID-17271178[0] automatic spike sorting for neural decoding
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{152} |
ref: Amirikian-2000.01
tags: Georgopulos directional tuning motor cortex SUA electrophysiology
date: 04-05-2007 16:34 gmt
revision:2
[1] [0] [head]
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PMID-10678534[0] Directional tuning profiles of motor cortical cells
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{295} |
ref: Georgopoulos-1982.11
tags: georgopoulos kalaska caminiti M1 motor control tuning population_vector
date: 04-05-2007 16:27 gmt
revision:0
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PMID-7143039[0] On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex
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{273} | |||||||||||||||||||||||||||||||
PMID-12879039[0] Military-funded research is not unethical heh! ____References____ | |||||||||||||||||||||||||||||||
{263} | |||||||||||||||||||||||||||||||
PMID-15811234[] Mirror Neurons Responding to Observation of Actions Made with Tools in Monkey Ventral Premotor Cortex
____References____ | |||||||||||||||||||||||||||||||
{260} | |||||||||||||||||||||||||||||||
Friday March 30 Jen shared an interesting algorithm for spike sorting: dist=pdist(psi); %This finds the Euclidean distances for all of the points (waveforms) in psi; %dist is of the form of a row vector of length m(m-1)/2. Could convert into a %distance matrix via squareform function, but is computationally inefficient. %m is the number of waveforms in psit. link=linkage(dist); %This performs a nearest neighbor linkage on the distance matrix and returns %a matrix of size (m-1)x3. Cols 1 and 2 contain the indices of the objects %were linked in pairs to form a new cluster. This new cluster is assigned the %index value m+i. There are m-1 higher clusters that correspond to the interior %nodes of the hierarchical cluster tree. Col 3 contains the corresponding linkage %distances between the objects paired in the clusters at each row i. [H,T]=dendrogram(link,0); %This creates a dendrogram; 0 instructs the function to plot all nodes in %the tree. H is vector of line handles, and T a vector of the cluster %number assignment for each waveform in psit. It looks real nice in theory, and computes very quickly on 2000 x 32 waveform data (provided you don't want to plot) -- however, I'm not sure if it works properly on synthetic data. Here are the commands that i tried: v = [randn(1000, 32); (randn(1000, 32) + rvecrep(ones(1,32),1000))]; [coef, vec] = pca(v); vv = v * vec(:, 1:2); dist = pdist(vv); link = linkage(dist); [H,T]=dendrogram(link,0); figure DensityPlotOpenGL(vv(:,1), vv(:,2)) -- the fitted dendogram, without PCA
-- the fitted dendogram, with PCA
-- the asociated PCA plot of the data, clearly showing two clusters. need to figure out how jen made the colorized plots | |||||||||||||||||||||||||||||||
{250} |
ref: engineering-0
tags: schematic capture layout PCB design engineering
date: 03-17-2007 23:44 gmt
revision:0
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{245} |
ref: AnguianoRodrAguez-2007.02
tags: serotonin learning dopamine
date: 03-12-2007 02:30 gmt
revision:0
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PMID-17126827 Striatal serotonin depletion facilitates rat egocentric learning via dopamine modulation. facilitates - they get better! (more awake than controls? inability to forget?) | |||||||||||||||||||||||||||||||
{244} | |||||||||||||||||||||||||||||||
PMID-17216714 Motor and cognitive functions of the neostriatum during bilateral blocking of its dopamine receptors
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{238} |
ref: SidibAc)-1997.06
tags: GPi anatomy retrograde tracing VL ventrolateral CM centromedian thalamus GPe striatum
date: 03-11-2007 06:08 gmt
revision:0
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PMID-9183697 Efferent connections of the internal globus pallidus in the squirrel monkey: I. Topography and synaptic organization of the pallidothalamic projection.
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{234} |
ref: Grabli-2004.09
tags: basal_ganglia gobus_pallidus pathology GPe
date: 03-11-2007 04:22 gmt
revision:0
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PMID-15292053 Behavioural disorders induced by external globus pallidus dysfunction in primates: I. Behavioural study.
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{230} |
ref: engineering notes-0
tags: homopolar generator motor superconducting magnet
date: 03-09-2007 14:39 gmt
revision:0
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http://hardm.ath.cx:88/pdf/homopolar.pdf
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{223} | |||||||||||||||||||||||||||||||
calculations for a strong DC loop magnet using 1/8" copper capillary tubing:
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{211} | |||||||||||||||||||||||||||||||
{27} |
ref: notes-0
tags: VOR OKR climbing_fibers cerebellum purkinje cells
date: 02-05-2007 23:45 gmt
revision:1
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{133} | |||||||||||||||||||||||||||||||
PMID-15727537 The mesocortical dopamine projection to anterior cingulate cortex plays no role in guiding effort-related decisions. | |||||||||||||||||||||||||||||||
{197} | |||||||||||||||||||||||||||||||
PMID-15151178[0] Sequential Rearrangements of the Ensemble Activity of Putamen Neurons in the Monkey Brain as a Correlate of Continuous Behavior
____References____ | |||||||||||||||||||||||||||||||
{143} | |||||||||||||||||||||||||||||||
PMID-17190032[0] http://hardm.ath.cx:88/pdf/Marzullo2006_CingulateCortexBMI.pdf
____References____ | |||||||||||||||||||||||||||||||
{23} |
ref: Vyssotski-2006.02
tags: neurologger neural_recording recording_technology EEG SUA LFP electrical engineering
date: 02-05-2007 06:21 gmt
revision:6
[5] [4] [3] [2] [1] [0] [head]
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PMID-16236777[0] Miniature neurologgers for flying pigeons: multichannel EEG and action and field potentials in combination with GPS recording. Recording neuronal activity of animals moving through their natural habitat is difficult to achieve by means of conventional radiotelemetry. This illustration shows a new approach, exemplified by a homing pigeon carrying both a small GPS path recorder and a miniaturized action and field potential logger (“neurologgerâ€), the entire assembly weighing maximally 35 g, a load carried easily by a pigeon over a distance of up to 50 km. Before release at a distant location, the devices are activated and store both positional and neuronal activity data during the entire flight. On return to the loft, all data are downloaded and can be analyzed using software for path analysis and electrical brain activity. Thus single unit activity or EEG patterns can be matched to the flight path superimposed on topographical maps. Such neurologgers may also be useful for a variety of studies using unrestrained laboratory animals in different environments or test apparatuses. The prototype on the hand-held pigeon records and stores EEG simultaneously from eight channels up to 47 h, or single unit activity from two channels during 9 h, but the number of channels can be increased without much gain in weight by sandwiching several of these devices. Further miniaturization can be expected. For details, see Vyssotski AL, Serkov AN, Itskov PM, Dell Omo G, Latanov AV, Wolfer DP, and Lipp H-P. Miniature neurologgers for flying pigeons: multichannel EEG and action and field potentials in combination with GPS recording. [1] ____References____ | |||||||||||||||||||||||||||||||
{7} |
ref: bookmark-0
tags: book information_theory machine_learning bayes probability neural_networks mackay
date: 0-0-2007 0:0
revision:0
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http://www.inference.phy.cam.ac.uk/mackay/itila/book.html -- free! (but i liked the book, so I bought it :) | |||||||||||||||||||||||||||||||
{15} |
ref: bookmark-0
tags: monte_carlo MCMC particle_filter probability bayes filtering biblography
date: 0-0-2007 0:0
revision:0
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http://www-sigproc.eng.cam.ac.uk/smc/papers.html -- sequential monte carlo methods. (bibliography)
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{22} |
ref: Brown-2001.11
tags: Huntingtons motor_learning intentional implicit cognitive deficits
date: 0-0-2007 0:0
revision:0
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PMID-11673321 http://brain.oxfordjournals.org/cgi/content/full/124/11/2188 :
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{29} | |||||||||||||||||||||||||||||||
Iterative Linear Quadratic regulator design for nonlinear biological movement systems
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{37} |
ref: bookmark-0
tags: Unscented sigma_pint kalman filter speech processing machine_learning SDRE control UKF
date: 0-0-2007 0:0
revision:0
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{105} | |||||||||||||||||||||||||||||||
{108} | |||||||||||||||||||||||||||||||
http://www.berndporr.me.uk/iso3_sab/
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{109} | |||||||||||||||||||||||||||||||
http://www.bcs.rochester.edu/people/alex/bcs547/readings/WolpertGhahr00.pdf
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{110} | |||||||||||||||||||||||||||||||
Iso learning approximates a solution to the inverse controller problem in an usupervised behavioral paradigm http://hardm.ath.cx/pdf/isolearning2002.pdf
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{111} |
ref: Wichmann-1999.04
tags: parkinsons basal ganglia substantia nigra
date: 0-0-2007 0:0
revision:0
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PMID-10323285 Comparison of MPTP-induced changes in spontaneous neuronal discharge in the internal pallidal segment and in the substania nigra pars reticulata
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{122} | |||||||||||||||||||||||||||||||
{127} |
ref: bookmark-0
tags: thalamus basal ganglia neuroanatomy centromedian red nucleus images
date: 0-0-2007 0:0
revision:0
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http://www.neuroanatomy.wisc.edu/coro97/contents.htm --coronal sections through the thalamus, very nice! | |||||||||||||||||||||||||||||||
{131} | |||||||||||||||||||||||||||||||
spindle neurons are found in the insular cortex as well as the anterior cingulate cortex, but only, apparently, in great apes. Activity in the insular cortex has been found to be correlated to feeling empathy. | |||||||||||||||||||||||||||||||
{140} | |||||||||||||||||||||||||||||||
PMID-15649663 Composite adaptive control with locally weighted statistical learning.
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{146} |
ref: van-2004.11
tags: anterior cingulate cortex error performance monitoring 2004
date: 0-0-2007 0:0
revision:0
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PMID-15518940 Errors without conflict: implications for performance monitoring theories of anterior cingulate cortex.
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{151} | |||||||||||||||||||||||||||||||
PMID-11741014 Computational approaches to motor control. Tamar Flash and Terry Sejnowski.
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{153} |
ref: Stefani-1995.09
tags: electrophysiology dopamine basal_ganglia motor learning
date: 0-0-2007 0:0
revision:0
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PMID-8539419 Electrophysiology of dopamine D-1 receptors in the basal ganglia: old facts and new perspectives.
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{155} |
ref: Wannier-2002.01
tags: globus_pallidus electrophysiology caudate putamen basal_ganglia
date: 0-0-2007 0:0
revision:0
[head]
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PMID-11924876 Neuronal activity in primate striatum and pallidum related to bimanual motor actions
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{157} | |||||||||||||||||||||||||||||||
PMID-14526085 Performance Monitoring by the Anterior Cingulate Cortex During Saccade Countermanding locations of neurons http://www.sciencemag.org/content/vol302/issue5642/images/large/se3831902004.jpeg | |||||||||||||||||||||||||||||||
{173} | |||||||||||||||||||||||||||||||
PMID-17259585 Giving up on unattainable goals: benefits for health?
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{191} | |||||||||||||||||||||||||||||||
PMID-14749432 Action Potential Timing Determines Dendritic Calcium during Striatal Up-States
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{4} | |||||||||||||||||||||||||||||||
{8} |
ref: bookmark-0
tags: machine_learning algorithm meta_algorithm
date: 0-0-2006 0:0
revision:0
[head]
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Boost learning or AdaBoost - the idea is to update the discrete distribution used in training any algorithm to emphasize those points that are misclassified in the previous fit of a classifier. sensitive to outliers, but not overfitting. | |||||||||||||||||||||||||||||||
http://www.linuxjournal.com/article/8497 here you go timmyh, enjoy.. /joeyo | |||||||||||||||||||||||||||||||
{20} |
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]
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http://www.ncrg.aston.ac.uk/netlab/index.php n.b. kinda old. (or does that just mean well established?) | |||||||||||||||||||||||||||||||
{36} | |||||||||||||||||||||||||||||||
{40} |
ref: bookmark-0
tags: Bayes Baysian_networks probability probabalistic_networks Kalman ICA PCA HMM Dynamic_programming inference learning
date: 0-0-2006 0:0
revision:0
[head]
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http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html very, very good! many references, well explained too. | |||||||||||||||||||||||||||||||
{42} |
ref: bookmark-0
tags: microdrilling surgery craniotomy impedance
date: 0-0-2006 0:0
revision:0
[head]
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http://www.pathscientific.com/products.html Pathformer is an electrosurgical hand-held meidcal device that cuts holes in nails and skin. It operates on mesoscissioning technology, cutting the nail/skin with a microcutting tool, using skin impedance as a feedback for stopping the cutting intervention. Pathformer is approved by FDA for creating holes in nails for treating subungual hematoma (black toe). | |||||||||||||||||||||||||||||||
http://www.iovs.org/cgi/reprint/46/4/1322.pdf A related machine learning classifier, the relevance vector machine (RVM), has recently been introduced, which, unlike SVM, incorporates probabalistic output (probability of membership) through Bayesian inference. Its decision function depends on fewer input variables that SVM, possibly allowing better classification for small data sets with high dimensionality.
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{44} | |||||||||||||||||||||||||||||||
http://www.jneurosci.org/cgi/reprint/24/12/2989.pdf
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{47} | |||||||||||||||||||||||||||||||
{50} |
ref: bookmark-0
tags: teflon PTFE bonding metal polytetrafluoroethylene tetraflouroethylene
date: 0-0-2006 0:0
revision:0
[head]
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http://pslc.ws/mactest/ptfeidea.htm block copolymer: http://en.wikipedia.org/wiki/Copolymer | |||||||||||||||||||||||||||||||
{51} | |||||||||||||||||||||||||||||||
http://www.ddj.com/dept/architect/184414658 very logical, well organized. | |||||||||||||||||||||||||||||||
{52} | |||||||||||||||||||||||||||||||
http://www.evl.uic.edu/eolmst1/GLSL/ easy to compile on my debian system - all the development libraries had debian packages! also of interest :
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{61} |
ref: bookmark-0
tags: smith predictor motor control wolpert cerebellum machine_learning prediction
date: 0-0-2006 0:0
revision:0
[head]
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http://prism.bham.ac.uk/pdf_files/SmithPred_93.PDF
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{63} | |||||||||||||||||||||||||||||||
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{64} |
ref: bookmark-0
tags: neural_recording recording_technology electrical engineering DSP
date: 0-0-2006 0:0
revision:0
[head]
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{66} |
ref: bookmark-0
tags: machine_learning classification entropy information
date: 0-0-2006 0:0
revision:0
[head]
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http://iridia.ulb.ac.be/~lazy/ -- Lazy Learning. | |||||||||||||||||||||||||||||||
{72} |
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]
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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. | |||||||||||||||||||||||||||||||
{85} | |||||||||||||||||||||||||||||||
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{92} | |||||||||||||||||||||||||||||||
with the extended kalman filter, from '92: http://ftp.ccs.neu.edu/pub/people/rjw/kalman-ijcnn-92.ps with the unscented kalman filter : http://hardm.ath.cx/pdf/NNTrainingwithUnscentedKalmanFilter.pdf |