m8ta
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{1578} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
God Help us, let's try to understand AI monosemanticity Commentary: To some degree, superposition seems like a geometric "hack" invented in the process of optimization to squeeze a great many (largely mutually-exclusive) sparse features into a limited number of neurons. GPT3 has a latent dimension of only 96 * 128 = 12288, and with 96 layers this is only 1.17 M neurons (*). A fruit fly has 100k neurons (and can't speak). All communication must be through that 12288 dimensional vector, which is passed through LayerNorm many times (**), so naturally the network learns to take advantage of locally linear subspaces. That said, the primate visual system does seem to use superposition, though not via local subspaces; instead, neurons seem to encode multiple axes somewhat linearly (e.g. global spaces: linearly combined position and class) That was a few years ago, and I suspect that new results may contest this. The face area seems to do a good job of disentanglement, for example. Treating everything as high-dimensional vectors is great for analogy making, like the wife - husband + king = queen example. But having fixed-size vectors for representing arbitrary-dimensioned relationships inevitably leads to compression ~= superposition. Provided those subspaces are semantically meaningful, it all works out from a generalization standpoint -- but this is then equivalent to allocating an additional axis for said relationship or attribute. Additional axes would also put less decoding burden on the downstream layers, and make optimization easier. Google has demonstrated allocation in transformers. It's also prevalent in the cortex. Trick is getting it to work! (*) GPT4 is unlikely to have more than an order of magnitude more 'neurons'; PaLM-540B has only 2.17 M. Given that GPT-4 is something like 3-4x larger, it should have 6-8 M neurons, which is still 3 orders of magnitude fewer than the human neocortex (nevermind the cerebellum ;-) (**) I'm of two minds on LayerNorm. PV interneurons might be seen to do something like this, but it's all local -- you don't need everything to be vector rotations. (LayerNorm effectively removes one degree of freedom, so really it's a 12287 dimensional vector) Update: After reading https://transformer-circuits.pub/2023/monosemantic-features/index.html, I find the idea of local manifolds / local codes to be quite appealing: why not represent sparse yet conditional features using superposition? This also expands the possibility of pseudo-hierarchical representation, which is great. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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? | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1572} |
ref: -2019
tags: Piantadosi cogntion combinators function logic
date: 09-05-2022 01:57 gmt
revision:0
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{1569} |
ref: -2022
tags: symbolic regression facebook AI transformer
date: 05-17-2022 20:25 gmt
revision:0
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Deep symbolic regression for recurrent sequences Surprisingly, they do not do any network structure changes; it’s Vaswini 2017w/ a 8-head, 8 layer transformer (sequence to sequence, not decoder only) with a latent dimension of 512. Significant work was in feature / representation engineering (e.g. base-10k representations of integers and fixed-precision representations of floating-point numbers. (both of these involve a vocabulary size of ~10k ... amazing still that this works..)) + the significant training regimen they worked with (16 Turing GPUs, 32gb ea). Note that they do perform a bit of beam-search over the symbolic regressions by checking how well each node fits to the starting sequence, but the models work even without this degree of refinement. (As always, there undoubtedly was significant effort spent in simply getting everything to work) The paper does both symbolic (estimate the algebraic recurence 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! Analysis of how the transformers work for these problems is weak; only one figure showing that the embeddings of the integers follows some meandering but continuous path in t-SNE space. Still, the trained transformer is able to usually best hand-coded sequence inference engine(s) in Mathematica, and does so without memorizing all of the training data. Very impressive and important result, enough to convince that this learned representation (and undiscovered cleverness, perhaps) beats human mathematical engineering, which probably took longer and took more effort. It follows, without too much imagination (but vastly more compute), that you can train an 'automatic programmer' in the very same way. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1567} |
ref: -0
tags: evolution simplicity symmetry kolmogorov complexity polyominoes protein interactions
date: 04-21-2022 18:22 gmt
revision:5
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Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution
The paper features a excellent set of references, including:
Letter to a friend following her article Machine learning in evolutionary studies comes of age Read your PNAS article last night, super interesting that you can get statistical purchase on long-lost evolutionary 'sweeps' via GANs and other neural network models. I feel like there is some sort of statistical power issue there? DNNs are almost always over-parameterized... slightly suspicious. This morning I was sleepily mulling things over & thought about a walking conversation that we had a long time ago in the woods of NC: Why is evolution so effective? Why does it seem to evolve to evolve? Thinking more -- and having years more perspective -- it seems almost obvious in retrospect: it's a consequence of Bayes' rule. Evolution finds solutions in spaces that have overwhelming prevalence of working solutions. The prior has an extremely strong effect. These representational / structural spaces by definition have many nearby & associated solutions, hence appear post-hoc 'evolvable'. (You probably already know this.) I think proteins very much fall into this category: AA were added to the translation machinery based on ones that happened to solve a particular problem... but because of the 'generalization prior' (to use NN parlance), they were useful for many other things. This does not explain the human-engineering-like modularity of mature evolved systems, but maybe that is due to the strong simplicity prior [1] Very very interesting to me is how the science of evolution and neural networks are drawing together, vis a vis the lottery ticket hypothesis. Both evince a continuum of representational spaces, too, from high-dimensional vectoral (how all modern deep learning systems work) to low-dimensional modular, specific, and general (phenomenological human cognition). I suspect that evolution uses a form of this continuum, as seen in the human high-dimensional long-range gene regulatory / enhancer network (= a structure designed to evolve). Not sure how selection works here, though; it's hard to search a high-dimensional space. The brain has an almost identical problem: it's hard to do 'credit assignment' in a billions-large, deep and recurrent network. Finding which set of synapses caused a good / bad behaviior takes a lot of bits. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Interactions between learning and evolution
Altogether (historically) interesting, but some of these ideas might well have been anticipated by some simple hand calculations. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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“Visualizing data using t-SNE”
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Some investigations into denoising models & their intellectual lineage: Deep Unsupervised Learning using Nonequilibrium Thermodynamics 2015
Generative Modeling by Estimating Gradients of the Data Distribution July 2019
Denoising Diffusion Probabilistic Models June 2020
Improved Denoising Diffusion Probabilistic Models Feb 2021
Diffusion Models Beat GANs on Image Synthesis May 2021
In all of above, it seems that the inverse-diffusion function approximator is a minor player in the paper -- but of course, it's vitally important to making the system work. In some sense, this 'diffusion model' is as much a means of training the neural network as it is a (rather inefficient, compared to GANs) way of sampling from the data distribution. In Nichol & Dhariwal Feb 2021, they use a U-net convolutional network (e.g. start with few channels, downsample and double the channels until there are 128-256 channels, then upsample x2 and half the channels) including multi-headed attention. Ho 2020 used single-headed attention only at the 16x16 level. Ho 2020 in turn was based on PixelCNN++
which is an improvement to (e.g. add selt-attention layers) Conditional Image Generation with PixelCNN Decoders
Most recently, GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Added text-conditional generation + many more parameters + much more compute to yield very impressive image results + in-painting. This last effect is enabled by the fact that it's a full generative denoising probabilistic model -- you can condition on other parts of the image! | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1556} |
ref: -0
tags: concept net NLP transformers graph representation knowledge
date: 11-04-2021 17:48 gmt
revision:0
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Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
Human-designed knowledge graphs are described here: ConceptNet 5.5: An Open Multilingual Graph of General Knowledge And employed for profit here: https://www.luminoso.com/ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
This paper describes a system for adaptively finding programs which succinctly and accurately produce desired output. These desired outputs are provided by the user / test system, and come from a number of domains:
Also in the lineage is the EC2 algorithm, which most of the same authors above published in 2018. EC2 centers around the idea of "explore - compress" : explore solutions to your program induction problem during the 'wake' phase, then compress the observed programs into a library by extracting/factoring out commonalities during the 'sleep' phase. This of course is one of the core algorithms of human learning: explore options, keep track of both what worked and what didn't, search for commonalities among the options & their effects, and use these inferred laws or heuristics to further guide search and goal-setting, thereby building a buffer attack the curse of dimensionality. Making the inferred laws themselves functions in a programming library allows hierarchically factoring the search task, making exploration of unbounded spaces possible. This advantage is unique to the program synthesis approach. This much is said in the introduction, though perhaps with more clarity. DreamCoder is an improved, more-accessible version of EC2, though the underlying ideas are the same. It differs in that the method for constructing libraries has improved through the addition of a powerful version space for enumerating and evaluating refactors of the solutions generated during the wake phase. (I will admit that I don't much understand the version space system.) This version space allows DreamCoder to collapse the search space for re-factorings by many orders of magnitude, and seems to be a clear advancement. Furthermore, DreamCoder incorporates a second phase of sleep: "dreaming", hence the moniker. During dreaming the library is used to create 'dreams' consisting of combinations of the library primitives, which are then executed with training data as input. These dreams are then used to train up a neural network to predict which library and atomic objects to use in given contexts. Context in this case is where in the parse tree a given object has been inserted (it's parent and which argument number it sits in); how the data-context is incorporated to make this decision is not clear to me (???). This neural dream and replay-trained neural network is either a GRU recurrent net with 64 hidden states, or a convolutional network feeding into a RNN. The final stage is a linear ReLu (???) which again is not clear how it feeds into the prediction of "which unit to use when". The authors clearly demonstrate that the network, or the probabalistic context-free grammar that it controls (?) is capable of straightforward optimizations, like breaking symmetries due to commutativity, avoiding adding zero, avoiding multiplying by one, etc. Beyond this, they do demonstrate via an ablation study that the presence of the neural network affords significant algorithmic leverage in all of the problem domains tested. The network also seems to learn a reasonable representation of the sub-type of task encountered -- but a thorough investigation of how it works, or how it might be made to work better, remains desired. I've spent a little time looking around the code, which is a mix of python high-level experimental control code, and lower-level OCaml code responsible for running (emulating) the lisp-like DSL, inferring type in it's polymorphic system / reconciling types in evaluated program instances, maintaining the library, and recompressing it using aforementioned version spaces. The code, like many things experimental, is clearly a work-in progress, with some old or unused code scattered about, glue to run the many experiments & record / analyze the data, and personal notes from the first author for making his job talks (! :). The description in the supplemental materials, which is satisfyingly thorough (if again impenetrable wrt version spaces), is readily understandable, suggesting that one (presumably the first) author has a clear understanding of the system. It doesn't appear that much is being hidden or glossed over, which is not the case for all scientific papers. With the caveat that I don't claim to understand the system to completion, there are some clear areas where the existing system could be augmented further. The 'recognition' or perceptual module, which guides actual synthesis of candidate programs, realistically can use as much information as is available in DreamCoder as is available: full lexical and semantic scope, full input-output specifications, type information, possibly runtime binding of variables when filling holes. This is motivated by the way that humans solve problems, at least as observed by introspection:
Critical to making this work is to have, as I've written in my notes many years ago, a 'self compressing and factorizing memory'. The version space magic + library could be considered a working example of this. In the realm of ANNs, per recent OpenAI results with CLIP and Dall-E, really big transformers also seem to have strong compositional abilities, with the caveat that they need to be trained on segments of the whole web. (This wouldn't be an issue here, as Dreamcoder generates a lot of its own training data via dreams). Despite the data-inefficiency of DNN / transformers, they should be sufficient for making something in the spirit of above work, with a lot of compute, at least until more efficient models are available (which they should be shortly; see AlphaZero vs MuZero). | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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 (??) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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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{1526} |
ref: -0
tags: neuronal assemblies maass hebbian plasticity simulation austria fMRI
date: 02-23-2021 18:49 gmt
revision:1
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PMID-32381648 A model for structured information representation in neural networks in the brain
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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" | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1530} |
ref: -2017
tags: deep neuroevolution jeff clune Uber genetic algorithms
date: 02-18-2021 18:27 gmt
revision:1
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Deep Neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning* Uber AI labs; Jeff Clune.
The result is indeed surprising, but it also feels lazy -- the total effort or information that they put into writing the actual algorithm is small; as mentioned in the introduction, this is a case of old algorithms with modern levels of compute. Analogously, compare Go-Explore, also by Uber AI labs, vs Agent57 by DeepMind; the Agent57 paper blithely dismisses the otherwise breathless Go-Explore result as feature engineering and unrealistic free backtracking / game-resetting (which is true..) It's strange that they did not incorporate crossover aka recombination, as David MacKay clearly shows that recombination allows for much higher mutation rates and much better transmission of information through a population. (Chapter 'Why have sex'). They also perhaps more reasonably omit developmental encoding, where network weights are tied or controlled through development, again in an analogy to biology. A better solution, as they point out, would be some sort of hybrid GA / ES / A3C system which used both gradient-based tuning, random stochastic gradient-based exploration, and straight genetic optimization, possibly all in parallel, with global selection as the umbrella. They mention this, but to my current knowledge this has not been done. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Discovering hidden factors of variation in deep networks
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{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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PMID-21527931 Two-photon absorption properties of fluorescent proteins
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Why deep learning works even though it shouldn't, instigated a fun thread thinking about "complexity of model" vs "complexity of solution".
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Stabilizing the lottery ticket hypothesis
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited
Random deep neural networks are biased towards simple functions
Reconciling modern machine learning practice and the bias-variance trade-off
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{1524} |
ref: -2020
tags: replay hippocampus variational autoencoder
date: 10-11-2020 04:09 gmt
revision:1
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Brain-inspired replay for continual learning with artificial neural networks
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{1521} |
ref: -2005
tags: dimensionality reduction contrastive gradient descent
date: 09-13-2020 02:49 gmt
revision:2
[1] [0] [head]
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Dimensionality reduction by learning and invariant mapping
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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
[2] [1] [0] [head]
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PMID-22542188 Clustered dynamics of inhibitory synapses and dendritic spines in the adult neocortex.
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{1478} |
ref: -2013
tags: 2p two photon STED super resolution microscope synapse synaptic plasticity
date: 08-14-2020 01:34 gmt
revision:3
[2] [1] [0] [head]
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PMID-23442956 Two-Photon Excitation STED Microscopy in Two Colors in Acute Brain Slices
PMID-29932052 Chronic 2P-STED imaging reveals high turnover of spines in the hippocampus in vivo | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1513} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Spent a while this evening reading about Qualified Immunity -- the law that permits government officials (e.g. police officers) immunity when 'doing their jobs'. It's perhaps one root of the George Floyd / racism protests, as it has set a precedent that US police can be violent and get away with it. (This is also related to police unions and collective liability loops... anyway) The supreme court has the option to take cases challenging the constitutionality of Qualified Immunity, which many on both sides of the political spectrum want them to do. It 'got' this power via Marbury vs. Madison. M v. M is self-referential genius:
As a person curious how systems gain complexity and feedback loops ... so much nerdgasm. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1512} |
ref: -0
tags: rutherford journal computational theory neumann complexity wolfram
date: 05-05-2020 18:15 gmt
revision:0
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The Structures for Computation and the Mathematical Structure of Nature
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{1510} |
ref: -2017
tags: google deepmind compositional variational autoencoder
date: 04-08-2020 01:16 gmt
revision:7
[6] [5] [4] [3] [2] [1] [head]
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SCAN: learning hierarchical compositional concepts
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{1511} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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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{1503} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Accelerated FRET-PAINT Microscopy
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{1498} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Optimization and functionalization of red-shifted rhodamine dyes
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{1497} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-26659050 Human level concept learning through probabalistic program induction
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{1496} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-29123069 A neural algorithm for a fundamental computing problem
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{1493} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-27690349 Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation
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{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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{1486} |
ref: -2019
tags: non degenerate two photon excitation fluorophores fluorescence OPO optical parametric oscillator
date: 10-31-2019 20:53 gmt
revision:0
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Efficient non-degenerate two-photon excitation for fluorescence microscopy | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1472} |
ref: -0
tags: computational neuroscience opinion tony zador konrad kording lillicrap
date: 07-30-2019 21:04 gmt
revision:0
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Two papers out recently in Arxive and Biorxiv:
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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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{1466} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Eminently useful. Source: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-974-fundamentals-of-photonics-quantum-electronics-spring-2006/lecture-notes/chapter7.pdf Laser Dye technology by Peter Hammond
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{1461} |
ref: -2019
tags: super-resolution microscopy fluorescent protein molecules
date: 05-28-2019 16:02 gmt
revision:3
[2] [1] [0] [head]
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PMID-30997987 Chemistry of Photosensitive Fluorophores for Single-Molecule Localization Microscopy
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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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{1455} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Conducting credit assignment by aligning local distributed representations
Lit review.
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{1441} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
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{1453} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-22325196 Backpropagation through time and the brain
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{1452} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-22325196 How Does the Brain Solve Visual Object Recognition
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{305} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-101388[0] Fine control of operantly conditioned firing patterns of cortical neurons.
____References____ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1451} |
ref: -2018
tags: sparse representation auditory cortex excitatation inhibition balance
date: 03-11-2019 20:47 gmt
revision:1
[0] [head]
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PMID-30307493 Sparse Representation in Awake Auditory Cortex: Cell-type Dependence, Synaptic Mechanisms, Developmental Emergence, and Modulation.
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{1444} |
ref: -2012
tags: parvalbumin interneurons V1 perceptual discrimination mice
date: 03-06-2019 01:46 gmt
revision:0
[head]
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PMID-22878719 Activation of specific interneurons improves V1 feature selectivity and visual perception
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{1440} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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{1438} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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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{1434} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Audio AI: isolating vocals from stereo music using Convolutional Neural Networks
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{1433} |
ref: -2008
tags: representational similarity analysis fMRI
date: 02-15-2019 02:27 gmt
revision:1
[0] [head]
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PMID-19104670 Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience
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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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{1419} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
All-optical machine learning using diffractive deep neural networks
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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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PMID-29375323 Fear learning regulates cortical sensory representation by suppressing habituation
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Curiosity-driven exploration by Self-supervised prediction
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{1416} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning data manifolds with a Cutting Plane method
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{1410} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Structure discovery in Nonparametric Regression through Compositional Kernel Search
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Coevolution of Fitness Predictors
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LDMNet: Low dimensional manifold regularized neural nets.
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PMID-21896383 Effect of Insertion Speed on Tissue Response and Insertion Mechanics of a Chronically Implanted Silicon-Based Neural Probe
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PMID-18336081 Adaptive integration in the visual cortex by depressing recurrent cortical circuits.
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PMID-28246640 Ultraflexible nanoelectronic probes form reliable, glial scar–free neural integration
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{1400} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-29220192 Fluidic Microactuation of Flexible Electrodes for Neural Recording.
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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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{1057} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-19666051[0] Insertion shuttle with carboxyl terminated self-assembled monolayer coatings for implanting flexible polymer neural probes in the brain.
____References____
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{1397} |
ref: -0
tags: polyimide electrodes immune response foreign body inflammation stiffener steiglitz
date: 12-28-2017 02:37 gmt
revision:0
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PMID-27534649 Intracortical polyimide electrodes with a bioresorbable coating.
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{1395} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-25574019 Biomaterials. Electronic dura mater for long-term multimodal neural interfaces.
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{1391} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Evolutionary Plasticity and Innovations in Complex Metabolic Reaction Networks
Summary thoughts: This is a highly interesting study, insofar that the authors show substantial support for their hypotheses that phenotypes can be explored through random-walk non-lethal mutations of the genotype, and this is somewhat invariant to the source of carbon for known biochemical reactions. What gives me pause is the use of linear programming / optimization when setting the relative concentrations of biomolecules, and the permissive criteria for accepting these networks; real life (I would imagine) is far more constrained. Relative and absolute concentrations matter. Still, the study does reflect some robustness. I suggest that a good control would be to ‘fuzz’ the list of available reactions based on statistical criteria, and see if the results still hold. Then, go back and make the reactions un-biological or less networked, and see if this destroys the measured degrees of robustness. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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
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{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
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{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
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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PMID-28084398 Highly Stable Glassy Carbon Interfaces for Long-Term Neural Stimulation and Low-Noise Recording of Brain Activity
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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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{1377} |
ref: -0
tags: nanopore membrane nanostraws melosh surface adhesion intracellular
date: 02-06-2017 23:34 gmt
revision:0
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PMID-22166016 Nanostraws for Direct Fluidic Intracellular Access
PMID-24710350, Quantification of nanowire penetration into living cells.
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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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{1367} |
ref: -0
tags: microstimulation rat cortex measurement ICMS spread
date: 01-26-2017 02:52 gmt
revision:0
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PMID-12878710 Spatiotemporal effects of microstimulation in rat neocortex: a parametric study using multielectrode recordings.
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{1366} |
ref: -0
tags: direct electrical stimulation neural mapping review
date: 01-26-2017 02:28 gmt
revision:0
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PMID-22127300 Direct electrical stimulation of human cortex -- the gold standard for mapping brain functions?
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{1355} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-25705966 Robust and fragile aspects of cortical blood flow in relation to the underlying angioarchitecture.
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{1364} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Aqueous degradation of polyimides
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{1305} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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{1362} |
ref: -0
tags: serial electron microscopy Lichtman reconstruction nervous tissue
date: 01-17-2017 23:32 gmt
revision:0
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PMID-26232230 Saturated Reconstruction of a Volume of Neocortex.
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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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PMID-22750248 In vivo effects of L1 coating on inflammation and neuronal health at the electrode-tissue interface in rat spinal cord and dorsal root ganglion.
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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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{1348} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Heller Lecture - Prof. David Kleinfeld
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{1347} |
ref: -0
tags: laser induced breakdown spectroscopy for surgery tissue differentiation
date: 09-22-2016 19:26 gmt
revision:0
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PMID-25426327 Laser induced breakdown spectroscopy for bone and cartilage differentiation - ex vivo study as a prospect for a laser surgery feedback mechanism.
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{1346} |
ref: -0
tags: super resolution imaging PALM STORM fluorescence
date: 09-21-2016 05:57 gmt
revision:0
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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
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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 . | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{711} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-19299587[0] Optical Deconstruction of Parkinsonian Neural Circuitry.
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{1340} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-26867734 A biophysical model of the cortex-basal ganglia-thalamus network in the 6-OHDA lesioned rat model of Parkinson’s disease
Overall, not a bad paper. Not very well organized, which is not assisted by the large amount of information presented, but having slogged through the figures, I'm somewhat convinced that the model is good. This despite my general reservations of these models: the true validation would be to have it generate actual behavior (and learning)! Lacking this, the approximations employed seem like a step forward in understanding how PD and DBS work. The results and discussion are consistent with {1255}, but not {711}, which found that STN projections from M1 (not the modulation of M1 projections to GPi, via efferents from STN) truly matter.
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{1339} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
* Watch the [http://homes.cs.washington.edu/~todorov/index.php?video=MordatchSIGGRAPH12&paper=Mordatch,%20SIGGRAPH%202012 movies! Discovery of complex behaviors through contact-invariant optimization]
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{1335} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
What are the concentrations of the monoamines in the brain? (Purpose: estimate the required electrochemical sensing area & efficiency)
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{1330} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
META II: Digital Vellum in the Digital Scriptorium: Revisiting Schorre's 1962 compiler-compiler
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{1328} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Utah/blackrock group has been working on improving the longevity of their parlyene encapsulation with the addition of ~50nm Al2O3.
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Problem: have a Q-switched Nd:YAG laser, (flashlamp pumped, passively Q-switched) from ebay (see this album). Allegedly it outputs 1J pulses of 8ns duration; in practice, it may put several 100mJ pulses ~ 16ns long while the flashlamp is firing. It was sold as a tattoo removal machine. However, I'm employing it to drill micro-vias in fine polyimide films. When focused through a 10x objective via the camera mount of an Leica microscope, 532nm (KTP doubled, second harmonic generation (SHG)) laser pulses both ablates the material, but does not leave a clean, sharp hole: it looks more like 'blasting': the hole is ragged, more like a crater. This may be from excessive 1064nm heating (partial KTP conversion), or plasma/flame heating & expansion due to absorption of the 532nm / 1064nm light. It may also be due to excessive pulse duration (should the laser not actually be q-switched... photodiode testing suggests otherwise, but I'd like to verify that), excessive pulse power, insufficient pulse intensity, or insufficient polyimide absorption at 532nm. The solution to excessive plasma and insufficient polyimide absorption is to shift the wavelength to 355nm (NUV) via third harmonic generation, 1064 + 532 = 355nm. This requires sum frequency generation (SFG), for which LBO (lithium triborate) or BBO (beta-barium borate) seem the commonly accepted nonlinear optical materials. To get SHG or THG, phase and polarization matching of the incoming light is critical. The output of the Nd:YAG laser is, I assume, non-polarized (or randomly polarized), as the KTP crystal simply screws on the front, and so should be rotationally agnostic (and there are no polarizing elements in the simple laser head -- unless the (presumed) Cr:YAG passive Q-switch induces some polarization.) Output polarization of the KTP crystal will be perpendicular to the incoming beam; if the resulting THG / SFG crystal needs Type-1 phase matching (both in phase and parallel polarization), will need a half-wave plate for 1064nm; for Type-II phase matching, no plate is needed. For noncritical phase matching in LBO (which I just bought), an oven is required to heat the crystal to the correct temperature. This suggests 73C for THG, while this suggests 150C (for SHG?). Third harmonic frequency generation by type-I critically phase-matched LiB3O5 crystal by means of optically active quartz crystal Suggests most lasers operate in Type-1 SHG, and Type-II THG, but this is less efficient than dual Type-1; the quartz crystal is employed to rotate the polarizations to alignment. Both SHG and THG crystals are heated for optimum power output. Finally, Short pulse duration of an extracavity sum-frequency mixing with an LiB3O5 (LBO) crystal suggests that no polarization change is required, nor oven control LBO temperature. Tight focus and high energy density is required, of course (at the expense of reduced crystal lifetime). Likely this is the Type-1,Type-II scheme alluded to in the paper above. I'll try this first before engaging further complexity (efficiency is not very important, as the holes are very small & material removal may be slow.) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Thin films and microelectrode arrays for neuroprosthetics
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PMID-25571176 Fabrication and characterization of a high-resolution neural probe for stereoelectroencephalography and single neuron recording. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Improvement in the adhesion of polyimide/epoxy joints using various curing agents
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Tantalum and chromium adhesion to polyimide. Part 2. Peel and locus of failure analyses
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Adhesion Evaluation of Adhesiveless Metal/Polyimide Substrate for MCM and high density packaging
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PMID-18640155 Characterization of flexible ECoG electrode arrays for chronic recording in awake rats.
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Kinetics of Alkaline Hydrolysis of a Polyimide Surface
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{1307} |
ref: -2000
tags: polyimide acrylic aluminum electro deposition imide insulation ultra thin
date: 02-27-2015 19:42 gmt
revision:0
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Ultrathin, Layered Polyamide and Polyimide Coatings on Aluminum
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Adhesiveless copper on polyimide substrate with nickel-chromium tiecoat
Adhesion Between Polymers and Other Substances - A Review of Bonding Mechanisms, Systems and Testing
High-Performance Polymers (book) Guy Rabilloud (via google books.)
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{1300} |
ref: -0
tags: Peter Ledochowitsch ECoG parylene fabrication MEMS
date: 09-25-2014 16:54 gmt
revision:0
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IEEE-5734604 (pdf) Fabrication and testing of a large area, high density, parylene MEMS µECoG array
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PMID-13539663 Subcortical threshold voltages as a function of sine wave frequencies Brown and Brackett
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PMID-4550167[1] Sensory responses elicited by subcortical high frequency electrical stimulation in man. -- everything innovative has already been done in the 70s!
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One of the goals/needs of the lab is to be able to stimluate and record nervous tissue at the same time. We do not have immediate access to optogenetic methods, but what about lower frequency EM stimulation? The idea: if you put the stimulation frequency outside the recording system bandwidth, there is no need to switch, and indeed no reason you can't stimulate and record at the same time. Hence, I very briefly checked for the effects of RF stimulation on nervous tissue.
Conclusion: worth a shot, especially given the paper by Alberts et al 1972.
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{1292} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-1937509 A silicon-based, three-dimensional neural interface: manufacturing processes for an intracortical electrode array. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1212} |
ref: Nordhausen-1994.02
tags: Utah array electrodes optimization
date: 08-14-2014 01:24 gmt
revision:2
[1] [0] [head]
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PMID-8180807[0] Optimizing recording capabilities of the Utah Intracortical Electrode Array.
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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
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Inductive Rule Learning on the Knowledge Level.
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Interfacial adhesion of polymeric coatings for microelectronic encapsulation
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Biomechanics of substrate boring by fig wasps
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Polyimide insulators for multilevel interconnections Arthur M. Wilson
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{1280} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Preparation of uniaxially oriented polyamide films by vacuum deposition polymerization
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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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{1275} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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{1269} |
ref: -0
tags: hinton convolutional deep networks image recognition 2012
date: 01-11-2014 20:14 gmt
revision:0
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{1267} |
ref: -0
tags: stretchable nanoparticle conductors gold polyurethane flocculation
date: 12-13-2013 02:12 gmt
revision:5
[4] [3] [2] [1] [0] [head]
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PMID-23863931 Stretchable nanoparticle conductors with self-organized conductive pathways.
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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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{1248} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Improved polyimide/metal adhesion by chemical modification approaches
IEEE-4936772 (pdf) Studies of adhesion of metal films to polyimide
IEEE-670747 (pdf) Adhesion evaluation of adhesiveless metal/polyimide substrate for MCM and high density packaging
On polyimide-polyimide interlayer adhesion: Diffusion and self-adhesion of the polyimide PMDA-ODA (1987)
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{1254} |
ref: -0
tags: woodchuck post-translational regulatory element
date: 09-30-2013 18:52 gmt
revision:2
[1] [0] [head]
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PMID-10074136 Woodchuck hepatitis virus posttranscriptional regulatory element enhances expression of transgenes delivered by retroviral vectors 1999
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PMID-23393413 Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease.
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{1252} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-20119944 Characterization of parylene C as an encapsulation material for implanted neural prostheses.
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{1249} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-21273316 Physiological clustering of visual channels in the mouse retina
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{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
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Environmental Aging and Deadhesion of Polyimide Dielectric films
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{1193} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-23010756[0] Comprehensive characterization and failure modes of tungsten microwire arrays in chronic neural implants.
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{835} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
So, a year and a half ago I bought a green 1995 900 SE convertible for $600. At that time, it didn't move or go in reverse. Since then, I've been fixing up random things here an there (or just straight modifying / breaking the car by other standards) and recently realized that I had better start keeping track of everything that's been done, in case my memory lapses or i need to know where some random part came from. I doubt this will be useful to anyone else - next time, pictures! Things that I've done to the green convertible, in approximate chronological order:
Things that need to be done to the 'vert:
Now, wonders of wonders, I have another of these cars - though a sedan, not a convertible. It cost much more (about 8x as much), and is hence in much better shape. That said, I've had to do the following:
And now the blue 1998 saab 900, sold to Adam:
Next, the saab 9000 aero:
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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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{1230} |
ref: -0
tags: dissertation interconnect parylene flexible electrodes
date: 02-26-2013 00:30 gmt
revision:2
[1] [0] [head]
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http://docs.lib.purdue.edu/dissertations/AAI3444877/
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{1231} |
ref: -0
tags: parylene interconnect monolithic integration silicon DRIE
date: 02-26-2013 00:29 gmt
revision:1
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PMID-11327505 Flexible Polyimide-Based Intracortical Electrode Arrays with Bioactive Capability
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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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{1177} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
IEEE-1196780 (pdf) 3D flexible multichannel neural probe array
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{1217} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-16921203[0] Effects of insertion conditions on tissue strain and vascular damage during neuroprosthetic device insertion.
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{1216} |
ref: Lee-2005.12
tags: micromotion silicon michigan array simulation strain
date: 01-28-2013 03:13 gmt
revision:1
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PMID-16317231[0] Biomechanical analysis of silicon microelectrode-induced strain in the brain.
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{1214} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-7972766 Brain and cerebrospinal fluid motion: real-time quantification with M-mode MR imaging.
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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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{746} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-10906696[0] Tissue response to single-polymer fibers of varying diameters: evaluation of fibrous encapsulation and macrophage density.
"
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{54} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
!:
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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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{1033} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-19703712[0] The insulation performance of reactive parylene films in implantable electronic devices.
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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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{1202} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-22254391 Chronic intracortical implantation of saccharose-coated flexible shaft electrodes into the cortex of rats. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1102} |
ref: Gilletti-2006.09
tags: electrode micromotion histology GFAP variable reluctance
date: 01-04-2013 02:28 gmt
revision:2
[1] [0] [head]
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PMID-16921202[0] Brain micromotion around implants in the rodent somatosensory cortex.
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{1190} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-16045910[0] Neuronal cell loss accompanies the brain tissue response to chronically implanted silicon microelectrode arrays.
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{1058} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-19596378 Magnetic insertion system for flexible electrode implantation.
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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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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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{1168} |
ref: -0
tags: debian linux github persistent ssh authentication
date: 07-27-2012 01:40 gmt
revision:1
[0] [head]
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If you don't want to repeatedly enter in your username/password for github when commiting, you'll want to enable an RSA authetication key. -- http://www.debian.org/devel/passwordlessssh run ssh-keygen(with no options). -- then https://help.github.com/articles/working-with-ssh-key-passphrases ssh-keygen -pwith your github passphrase (I'm not totally sure this is essential). For me, pull and push aftwerard worked without needing to supply my password. Easy! | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1157} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-22448159 Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational Bayes.
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{967} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-22017994[0] Closed-loop deep brain stimulation is superior in ameliorating parkinsonism.
Other thoughts:
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{1156} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-15496658 Neuronal oscillations in the basal ganglia and movement disorders: evidence from whole animal and human recordings.
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{1154} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-21696996 The hippocampus: hub of brain network communication for memory
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{1153} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-11832222 Theta Oscillations in the Hippocampus
Original model for theta oscillation creation (figure 2):
LTP:
Conclusions:
Misc:
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{1152} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
http://web.cecs.pdx.edu/~greenwd/xmsnLine_notes.pdf -- Series termination will work, provided the impedance of the driver + series resistor is matched to the impedance of the transmission line being driven. School has been so long ago, I've forgotten these essentials! | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{696} |
ref: Jarosiewicz-2008.12
tags: Schwartz BMI learning perturbation
date: 03-07-2012 17:11 gmt
revision:2
[1] [0] [head]
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PMID-19047633[0] Functional network reorganization during learning in a brain-computer interface paradigm.
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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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{969} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-19460368[0] Pathological subthalamic nucleus oscillations in PD: can they be the cause of bradykinesia and akinesia?
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{991} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-19299613[0] Spinal cord stimulation restores locomotion in animal models of Parkinson's disease.
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{241} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-17046697 Rapid alterations in corticostriatal ensemble coordination during acute dopamine-dependent motor dysfunction.
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{1149} |
ref: -0
tags: locomotion decerebrated monkeys spinal cord section STN stimulation
date: 03-01-2012 23:53 gmt
revision:0
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PMID-7326562 Locomotor control in macaque monkeys
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{232} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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{1145} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-19109506 Parkinsonian beta oscillations in the external globus pallidus and their relationship with subthalamic nucleus activity.
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{1064} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-18448656[0] Disrupted dopamine transmission and the emergence of exaggerated beta oscillations in subthalamic nucleus and cerebral cortex.
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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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{121} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-2402638[] Reversal of experimental parkinsonism by lesions of the subthalamic nucleus.
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{1146} |
ref: -0
tags: oscillations DBS globus pallidus parkinsons
date: 02-28-2012 17:24 gmt
revision:1
[0] [head]
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PMID-17880401 Late emergence of synchronized oscillatory activity in the pallidum during progressive Parkinsonism.
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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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{1143} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-11052216 Organization of the basal ganglia: the importance of axonal collateralization.
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{1140} |
ref: -0
tags: dopamine reward prediction striatum error striatum orbitofrontal reward
date: 02-24-2012 21:26 gmt
revision:1
[0] [head]
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PMID-11105648 Involvement of basal ganglia and orbitofrontal cortex in goal-directed behavior.
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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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{1139} |
ref: -0
tags: striatum microstimulation abnormal myclonus dyskinesia
date: 02-24-2012 19:44 gmt
revision:0
[head]
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PMID-21508304 Discontinuous Long-Train Stimulation in the Anterior Striatum in Monkeys Induces Abnormal Behavioral States
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{1070} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-19955287[0] Deep brain stimulation does not silence neurons in subthalamic nucleus in Parkinson's patients.
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{1088} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-18540149[0] Deep brain stimulation: how does it work?
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{1083} |
ref: Holgado-2010.09
tags: DBS oscillations beta globus pallidus simulation computational model
date: 02-22-2012 18:36 gmt
revision:4
[3] [2] [1] [0] [head]
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PMID-20844130[0] Conditions for the Generation of Beta Oscillations in the Subthalamic Nucleus–Globus Pallidus Network
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PMID-21810782[0] Deep Brain Stimulation of Subthalamic Nuclei Affects Arm Response Inhibition In Parkinson’s Patients
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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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PMID-12077209[0][] High-frequency stimulation of the subthalamic nucleus selectively reverses dopamine denervation-induced cellular defects in the output structures of the basal ganglia in the rat.
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PMID-9835393 Role of dynorphin and enkephalin in the regulation of striatal output pathways and behavior.
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PMID-12023310[0] Dependence of subthalamic nucleus oscillations on movement and dopamine in Parkinson's disease.
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PMID-21723919[0] Pathological basal ganglia activity in movement disorders.
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PMID-12495873[0] Relationship between oscillations in the basal ganglia and synchronization of cortical activity.
Also, random: the world's highest rate of Parkinson's disease is in the Amish in the NE US. More than twice that of anywhere else; http://www.viartis.net/parkinsons.disease/news/090801.htm ____References____
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PMID-12671940[0] Oscillatory nature of human basal ganglia activity: relationship to the pathophysiology of Parkinson's disease.
Conclusion: he really thinks that there is a strong dichotomy between HF, pro-kinetic, and MF, anti-kinetic oscillations. ____References____
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PMID-12220881[] Move to the rhythm: oscillations in the subthalamic nucleus-external globus pallidus network.
my notes:
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PMID-19559747[0] Deep brain stimulation in neurological diseases and experimental models: from molecule to complex behavior.
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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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There seems to be an interesting connection between excessive grip force, isometric muscle contraction causing coherence between motor cortex and EMG, lack of inhibition on delayed response and go-no-go task, and experiments with STN lesioned rats, and the high/low oscillation hypothesis. Rather tenuous, I suppose, but let me spell it out. ( My personal impression, post-hoc, is that this is an epiphenomena of something else; evidence is contradictory.)
footnote: how much is our search for oscillations informed by our available analytical techniques? Hypothesis: Impulsivity may be the cognitive equivalent of excess grip force; maintenance of consistent 'force' or delayed decision making benefits from Piper-band rhythms, something which PD abolishes (gradually, through brain adaptation). DBS disrupts the beta (resting, all synchronized) rhythm, and thereby permits movement. However it also effectively 'lesions' the STN, which leads to cognitive deficits and poor force control. (Wait .. DBS plus levodopa improves 40-60Hz energy -- this would argue against the hypothesis. Also, stroke in the STN in normal individuals causes hemiballismus, which resolves gradually; this is not consistent with oscillations, but rather connectivity and activity.) Testing this hypothesis: well, first of all, is there beta-band oscillations in our data? what about piper band? We did not ask the patients to delay response, so any tests thereof will be implicit. Can look at relative energy 10Hz-30Hz and 30Hz-60Hz in the spike traces & see if this is modulated by hand position. (PETH as usual). So. I made PETHs for beta / gamma power ratio of the spiking rate, controlled by shuffling the PETH triggers. Beta power was between 12 and 30 Hz; gamma between 30 and 75 Hz, as set by a noncausal IIR bandpass filter. The following is a non-normalized heatmap of all significant PETHs over all sessions triggered when the hand crossed the midpoint between targets. (A z-scored heatmap was made as well; it looked worse). X is session number, Y time, 0 = -1 sec. sampling rate = 200 Hz. In one file (the band) there seems to be selective gamma inhibition about 0.5 sec before peak movement. Likely it is an outlier. 65 neurons of 973 (single and multiunits together) were significantly 'tuned' = 6.6%; marginally significant by binomial test (p=0.02). Below is an example PETH, with the shuffled distribution represented by mean +- 1 STD in blue. The following heatmap is created from the significant PETHs triggered on target appearance. 80 of the 204 significant PETHs are from PLEX092606005_a. The total number of significant responses (204/1674, single units and multiunits) is significant by the binomial test p < 0.001, with and without Sept. 26 removed. Below is an example plot (092606005). Looks pretty damn good, actually. Let's see how stable this relationship is by doing a leave-half out cross-validation, 10 plies, in red below (all triggers plotted in black) Looks excellent! Problem is we are working with a ratio, which is prone to spikes. Fix: work in log space. Aggregate response remains about the same. 192 / 1674 significant (11.5%) In the above figure, positive indicates increased power relative to power. The square shape is likely relative to (negative lags) hold time and (positive lags) reaction time, though the squareness is somewhat concerning. Recording is from VIM. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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I recently wrote a matlab script to measure & plot the autocorrelation of a spike train; to test it, I generated a series of timestamps from a homogeneous Poisson process: function [x, isi]= homopoisson(length, rate) % function [x, isi]= homopoisson(length, rate) % generate an instance of a poisson point process, unbinned. % length in seconds, rate in spikes/sec. % x is the timestamps, isi is the intervals between them. num = length * rate * 3; isi = -(1/rate).*log(1-rand(num, 1)); x = cumsum(isi); %%find the x that is greater than length. index = find(x > length); x = x(1:index(1,1)-1, 1); isi = isi(1:index(1,1)-1, 1); The autocorrelation of a Poisson process is, as it should be, flat: Above:
The problem with my recordings is that there is generally high long-range correlation, correlation which is destroyed by shuffling. Above is a plot of 1/isi for a noise channel with very high mean 'firing rate' (> 100Hz) in blue. Behind it, in red, is 1/shuffled isi. Noise and changes in the experimental setup (bad!) make the channel very non-stationary. Above is the autocorrelation plotted in the same way as figure 1. Normally, the firing rate is binned at 100Hz and high-pass filtered at 0.005hz so that long-range correlation is removed, but I turned this off for the plot. Note that the suffled data has a number of different offsets, primarily due to differing long-range correlations / nonstationarities. Same plot as figure 3, with highpass filtering turned on. Shuffled data still has far more local correlation - why? The answer seems to be in the relation between individual isis. Shuffling isi order obviuosly does not destroy the distribution of isi, but it does destroy the ordering or pair-wise correlation between isi(n) and isi(n+1). To check this, I plotted these two distributions: -- Original log(isi(n)) vs. log(isi(n+1) -- Shuffled log(isi_shuf(n)) vs. log(isi_shuf(n+1) -- Close-up of log(isi(n)) vs. log(isi(n+1) using alpha-blending for a channel that seems heavily corrupted with electro-cauterizer noise. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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We have found the following types of neurons during acute intraoperative recrodings from the subthalamic nucleus (STN) of awake parkinson's patients. During the surgeries the patients opened and closed their hand, instrumented through a virtual-reality data glove, in order to move a cursor to randomly presented targets in a 1-dimensional field.
This plot shows a neuron which fires preferentially when a target appears and the patient moves to the left (again, in this graph: y = -1 indicates target appears to the left, + 1 target to the right, and 0 otherwise). Note that there is noticable oscillations, due to the fact that the patient's behavior was very periodic, with a period of around 2 seconds. The neuron was inhibited around the instant of target apperance, independent of direction, as indicated by the blue regions at y = -1 and 1 around lag 0.
This plot shows a neuron which is inhibited just before target apperance (in this plot, y = 1 400ms around target appearance, independent of direction). That is, the neuron stops firing upon sucessful completion of a movement. This neuron shows no pathological oscillatory tuning; therefore, it might be assumed that not all of the STN is incapacitated by Parkinson's disease.
Here is another example of a neuron that does not show oscillatory firing behavior. In this graph, y = 1 when the patient is opening or closing his hand (equivalently the cursor velocity exceeds a threshold); y = 0 otherwise. This neuron is therefore inhibited during periods of movement. Note that around a lag of 2.5 seconds, the neuron has a higher probability of firing (the red region), possibly indicating positive firing upon successful completion of a movement.
Another example of a neuron that is tuned to thresholded cursor velocity, though this time, the firing rate becomes positive just around the instant of movement. Note here there is evidence of highly periodic behavior, as seen in the green/yellow regions spaced about 1.6 seconds apart along y=1. The region at lag = 1.6 secons corresponds to the movement following target acquisition, hence exhibits a higher firing rate.
This neuron, like the one above, fires strongly whenever the hand moves. Interestingly, there appeared to be no directional information in either of these cells.
Finally, we discovered that there appears to be error-correlated firing within the STN. The neuron shown above is selectively inhibited around periods where the cursor and target positions differ. In the plot above y=1 indicates the absolute value of the target position - the cursor position exceeds a threshold of 20% of the total range (this is subtly different from the target apperance signal, as the patient can over shoot or under shoot the target position with the cursur, upon which this signal will be 1. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1123} |
ref: Berke-2009.09
tags: DBS oscillations high gamma synchronization
date: 02-16-2012 17:48 gmt
revision:1
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PMID-19659455[0] Fast oscillations in cortical-striatal networks switch frequency following rewarding events and stimulant drugs.
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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.
____References____ Snow, S. and Jacobsen, S.C. and Wells, D.L. and Horch, K.W. Microfabricated cylindrical multielectrodes for neural stimulation Biomedical Engineering, IEEE Transactions on 53 2 320 -326 (2006) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{1110} |
ref: -0
tags: Seymour thesis electrode lithography fabrication
date: 02-05-2012 17:35 gmt
revision:4
[3] [2] [1] [0] [head]
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Advanced polymer-based microfabricated neural probes using biologically driven designs.
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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-11571334[0] Clinical characteristics and topography of lesions in movement disorders due to thalamic lesions ____References____
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PMID-16317234 A finite-element model of the mechanical effects of implantable microelectrodes in the cerebral cortex.
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PMID-18842415[0] Transient hemiballism caused by a small lesion of the subthalamic nucleus.
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PMID-18562098[0] Accurate timing but increased impulsivity following excitotoxic lesions of the subthalamic nucleus.
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PMID-20727974[0] Bursts and oscillations as independent properties of neural activity in the parkinsonian globus pallidus internus.
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{753} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-14960502[0] Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance.
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{172} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-15249649 Involvement of the human subthalamic nucleus in movement preparation
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PMID-11704255[0] Unilateral lesions of the pedunculopontine nucleus do not alleviate subthalamic nucleus-mediated anticipatory responding in a delayed sensorimotor task in the rat.
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PMID-9189903[] Subthalamic stimulation elicits hemiballismus in normal monkey.
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PMID-17962524[0] Hold your horses: impulsivity, deep brain stimulation, and medication in parkinsonism.
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{162} |
ref: Krack-2001.09
tags: STN subthalamic nucleus stimulation PD parkinsons DBS
date: 01-24-2012 05:48 gmt
revision:1
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PMID-11746616[0] Mirthful laughter induced by subthalamic nucleus stimulation.
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{163} |
ref: Hilker-2004.01
tags: STN subthalamic DBS stimulation cortex cerebellum PET PD parkinsons
date: 01-24-2012 05:38 gmt
revision:1
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PMID-14688612[0] Subthalamic Nucleus Stimulation Restores Glucose Metabolism in Associative and Limbic Cortices and in Cerebellum: Evidence from a FDG-PET Study in Advanced Parkinson's Disease
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PMID-6869036[0] The Piper rhythm--a phenomenon related to muscle resonance characteristics?
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PMID-11765129[0] Cortical network resonance and motor activity in humans.
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PMID-20937936[0] Deep brain stimulation for Parkinson disease: an expert consensus and review of key issues.
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PMID-11923450[0] Synchronized neuronal discharge in the basal ganglia of parkinsonian patients is limited to oscillatory activity.
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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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{931} |
ref: Deco-2009.05
tags: stochastic dynamics Romo memory computation
date: 01-16-2012 18:54 gmt
revision:1
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PMID-19428958[0] Stochastic dynamics as a principle of brain function
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PMID-8036499[0] Direct cortical representation of drawing ____References____
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PMID-21719340 Modelization of a self-opening peripheral neural interface: a feasibility study. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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I've recently tried to determine the bit-rate of conveyed by one gaussian random process about another in terms of the signal-to-noise ratio between the two. Assume is the known signal to be predicted, and is the prediction. Let's define where . Note this is a ratio of powers; for the conventional SNR, . is also known as the mean-squared-error (mse). Now, ; assume x and y have unit variance (or scale them so that they do), then
We need the covariance because the mutual information between two jointly Gaussian zero-mean variables can be defined in terms of their covariance matrix: (see http://www.springerlink.com/content/v026617150753x6q/ ). Here Q is the covariance matrix,
Then or This agrees with intuition. If we have a SNR of 10db, or 10 (power ratio), then we would expect to be able to break a random variable into about 10 different categories or bins (recall stdev is the sqrt of the variance), with the probability of the variable being in the estimated bin to be 1/2. (This, at least in my mind, is where the 1/2 constant comes from - if there is gaussian noise, you won't be able to determine exactly which bin the random variable is in, hence log_2 is an overestimator.) Here is a table with the respective values, including the amplitude (not power) ratio representations of SNR. "
Now, to get the bitrate, you take the SNR, calculate the mutual information, and multiply it by the bandwidth (not the sampling rate in a discrete time system) of the signals. In our particular application, I think the bandwidth is between 1 and 2 Hz, hence we're getting 1.6-3.2 bits/second/axis, hence 3.2-6.4 bits/second for our normal 2D tasks. If you read this blog regularly, you'll notice that others have achieved 4bits/sec with one neuron and 6.5 bits/sec with dozens {271}. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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-4196269[0] Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles
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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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PMID-9537321[0] Somatosensory discrimination based on cortical microstimulation.
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Recently I've been working on a current-controlled microstimulator for the lab, and have not been at all satisfied with the performance - hence, I decided to redesign it. Since it is a digitally current-controlled stimulator, and the current is set with a DAC (MCP4822), we need a voltage controlled current source. Here is one design:
What I really need is a high-side regulated current source; after some fiddling, here is what I came up with:
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{966} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-6077726[0] The limbic system and behavioral reinforcement
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{348} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{901} |
ref: Zacksenhouse-2007.07
tags: Zacksenhouse 2007 Odoherty Nicolelis cortical adaptation BMI
date: 01-06-2012 03:10 gmt
revision:3
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PMID-17637835[0] Cortical modulations increase in early sessions with brain-machine interface.
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{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?) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{349} |
ref: thesis-0
tags: clementine 042007 operant conditioning biofeedback tlh24
date: 01-06-2012 03:08 gmt
revision:4
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{222} |
ref: neuro notes-0
tags: clementine thesis electrophysiology fit predictions tlh24
date: 01-06-2012 03:07 gmt
revision:4
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ok, so i fit all timestamps from clem022007001 & timarm_log_070220_173947_k.mat to clementine's behavior, and got relatively low SNR for almost everything - despite the fact that I am most likely overfitting. (bin size = 7802 x 1491) the offset is calibrated @ 2587 ms + 50 to center the juice artifact in the first bin. There are 10 lags. There are 21 sorted units. same thing, but with only the sorted units. juice prediction is, of course, worse. now, for file clem022007002 & timarm_log_070220_175636_k.mat. first the unsorted: and the sorted: | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{929} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-17694874[0] The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.
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{949} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-18509337[0] Cortical control of a prosthetic arm for self-feeding
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{1021} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-4207598[0] Behavioral control of firing patterns of normal and abnormal neurons in chronic epileptic cortex.
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{940} |
ref: Fritsch-1870
tags: Fritsch Hitzig 1870 electrical stimulation
date: 01-03-2012 23:31 gmt
revision:2
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PMID-19457461[0] Electric excitability of the cerebrum (Uber die elektrische Erregbarkeit des Grosshirns).
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{240} |
ref: MolinaLuna-2007.03
tags: ICMS microstimulation cortical thin-film electrodes histology MEA
date: 01-03-2012 22:54 gmt
revision:2
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PMID-17178423[0] Cortical stimulation mapping using epidurally implanted thin-film microelectrode arrays.
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{941} |
ref: Penfield-1937
tags: Penfield 1937 motor cortex stimulation ICMS human neurosurgery electrodes
date: 01-03-2012 22:08 gmt
revision:3
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No PMID / bibtex penfield-1937. Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation
The account of Bartholow (1874) is interesting to say the least and may be cited. His patient was a 30-year old-domestic. As an infant this unfortunate had chanced to fall into the fire, burning her scalp so badly that " hair was never reproduced." A piece of whale bone in the wig she was forced to wear irritated the scarred scalp and, by her statement, three months before she was admitted, an ulcer appeared. When she presented herself for relief, this had eroded the skull over a space 2 in. in diameter " where the pulsations of the brain are plainly seen." Although " rather feeble-minded " Bartholow observed that Mary returned replies to all questions and no sensory or motor loss could be made out in spite of the fact that brain substance apparently had been injured in the process of evacuation of pus from the infected area. The doctor believed, therefore, that fine insulated needles could be introduced without further damage. While the electrodes were in the right side Bartholow decided to try the effect of more current. ' Her countenance exhibited great distress and she began to cry. Very soon the left hand was extended as if in the act of taking hold of some object in front of her; the arm presently was agitated with clonic spasms ; her eyes became fixed with pupils widely dilated ; the lips were blue and she frothed at the mouth ; her breathing became stertorous, she lost conscious-ness and was violently convulsed on the left side. This convulsion lasted for five minutes and was succeeded by coma. She returned to consciousness in twenty minutes from the beginning of the attack and complained of some weakness and vertigo." Three days after this stimulation, following a series of right-sided seizures, the patient died.
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{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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{977} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
bibtex: delgado-1964 Personality, education, and electrical stimulation of the brain
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{975} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-13367871[0] Conditioned reflexes established to electrical stimulation of cat cerebral cortex.
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{976} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-14347624[0] Conditioned reflexes elicited by Eletrical Stimulation of the Brain in Macaques
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{981} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-19762428[0] Auditory midbrain implant: a review.
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{984} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
IEEE-6114258 (pdf) Towards a Brain-Machine-Brain Interface:Virtual Active Touch Using Randomly Patterned Intracortical Microstimulation.
____References____ O'Doherty, J. and Lebedev, M. and Li, Z. and Nicolelis, M. Towards a Brain #x2013;Machine #x2013;Brain Interface:Virtual Active Touch Using Randomly Patterned Intracortical Microstimulation Neural Systems and Rehabilitation Engineering, IEEE Transactions on PP 99 1 (2011) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{987} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-19750199[0] A brain-machine interface instructed by direct intracortical microstimulation.
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{988} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-17419757[0] Detection psychophysics of intracortical microstimulation in rat primary somatosensory cortex.
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{585} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
LMS-based adaptive decorrelator, xn is the noise, xs is the signal, len is the length of the signal, delay is the delay beyond which the autocorrelation function of the signal is zero but the acf of the noise is non-zero. The filter is very simple, and should be easy to implement in a DSP. function [y,e,h] = lms_test(xn, xs, len, delay) h = zeros(len, 1); x = xn + xs; for k = 1:length(x)-len-delay y(k) = x(k+delay:k+len-1+delay) * h ; e(k) = x(k) - y(k); h = h + 0.0004 * e(k) * x(k+delay:k+len-1+delay)'; endIt works well if the noise source is predictable & stable: (black = sinusoidal noise, red = output, green = error in output) Now, what if the amplitude of the corrupting sinusoid changes (e.g. due to varying electrode properties during movement), and the changes per cycle are larger than the amplitude of the signal? The signal will be swamped! The solution to this is to adapt the decorrelating filter slowly, by adding an extra (multiplicative, nonlinear) gain term to track the error in terms of the absolute values of the signals (another nonlinearity). So, if the input signal is on average larger than the output, the gain goes up and vice-versa. See the code. function [y,e,h,g] = lms_test(xn, xs, len, delay) h = zeros(len, 1); x = xn + xs; gain = 1; e = zeros(size(x)); e2 = zeros(size(x)); for k = 1:length(x)-len-delay y(k) = x(k+delay:k+len-1+delay) * h; e(k) = (x(k) - y(k)); h = h + 0.0002 * e(k) * x(k+delay:k+len-1+delay)'; % slow adaptation. y2(k) = y(k) * gain; e2(k) = abs(x(k)) - abs(y2(k)); gain = gain + 1 * e2(k) ; gain = abs(gain); if (gain > 3) gain = 3; end g(k) = gain; end If, like me, you are interested in only the abstract features of the signal, and not an accurate reconstruction of the waveform, then the gain signal (g above) reflects the signal in question (once the predictive filter has adapted). In my experiments with a length 16 filter delayed 16 samples, extracting the gain signal and filtering out out-of-band information yielded about +45db improvement in SNR. This was with a signal 1/100th the size of the disturbing amplitude-modulated noise. This is about twice as good as the human ear/auditory system in my tests.
It doesn't look like much, but it is just perfect for EMG signals corrupted by time-varying 60hz noise. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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PMID-4966614 Relation of pyramidal tract activity to force exerted during voluntary movement.
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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
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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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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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{760} |
ref: -0
tags: LDA myopen linear discriminant analysis classification
date: 01-03-2012 02:36 gmt
revision:2
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How does LDA (Linear discriminant analysis) work? It works by projecting data points onto a series of planes, one per class of output, and then deciding based which projection plane is the largest. Below, to the left is a top-view of this projection with 9 different classes of 2D data each in a different color. Right is a size 3D view of the projection - note the surfaces seem to form a parabola. Here is the matlab code that computes the LDA (from myopen's ceven % TrainData and TrainClass are inputs, column major here. % (observations on columns) N = size(TrainData,1); Ptrain = size(TrainData,2); Ptest = size(TestData,2); % add a bit of interpolating noise to the data. sc = std(TrainData(:)); TrainData = TrainData + sc./1000.*randn(size(TrainData)); K = max(TrainClass); % number of classes. %%-- Compute the means and the pooled covariance matrix --%% C = zeros(N,N); for l = 1:K; idx = find(TrainClass==l); % measure the mean per class Mi(:,l) = mean(TrainData(:,idx)')'; % sum all covariance matrices per class C = C + cov((TrainData(:,idx)-Mi(:,l)*ones(1,length(idx)))'); end C = C./K; % turn sum into average covariance matrix Pphi = 1/K; Cinv = inv(C); %%-- Compute the LDA weights --%% for i = 1:K Wg(:,i) = Cinv*Mi(:,i); % this is the slope of the plane Cg(:,i) = -1/2*Mi(:,i)'*Cinv*Mi(:,i) + log(Pphi)'; % and this, the origin-intersect. end %%-- Compute the decision functions --%% Atr = TrainData'*Wg + ones(Ptrain,1)*Cg; % see - just a simple linear function! Ate = TestData'*Wg + ones(Ptest,1)*Cg; errtr = 0; AAtr = compet(Atr'); % this compet function returns a sparse matrix with a 1 % in the position of the largest element per row. % convert to indices with vec2ind, below. TrainPredict = vec2ind(AAtr); errtr = errtr + sum(sum(abs(AAtr-ind2vec(TrainClass))))/2; netr = errtr/Ptrain; PeTrain = 1-netr; | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{771} |
ref: -0
tags: procreation babies commentary demography
date: 01-03-2012 02:36 gmt
revision:2
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Demography: Babies make a comeback
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PMID-7760138[0] Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons
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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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{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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{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) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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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{738} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-15651568[0] A compact large voltage-compliance high output-impedance programmable current source for implantable microstimulators.
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PMID-21976021[0] Active tactile exploration using a brain-machine-brain interface.
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{974} |
ref: Fitzsimmons-2007.05
tags: Fitzsimmons nicolelis stimluation ICMS owl monkeys
date: 01-01-2012 00:12 gmt
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PMID-17522304[0] Primate reaching cued by multichannel spatiotemporal cortical microstimulation.
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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
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hunch.net interesting posts:
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PMID-4888623[0] Electrical stimulation of the brain in behavioral context.
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PMID-14090522[0] The Brains record of auditory and visual experience -- A final summary and discussion
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{962} |
ref: Harris-2009.06
tags: Bartholow 1874 Mary experiment stimulation ICMS
date: 12-29-2011 05:13 gmt
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PMID-19286295[0] Probing the human brain with stimulating electrodes: The story of Roberts Bartholow’s (1874) experiment on Mary Rafferty
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PMID-4598035[0] Operant conditioning of single-unit response patterns in visual cortex.
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{60} |
ref: Douglas-1991.01
tags: functional microcircuit cat visual cortex microstimulation
date: 12-29-2011 05:12 gmt
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PMID-1666655[0] A functional microcircuit for cat visual cortex
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PMID-8815302[0] Electrical stimulation of neural tissue to evoke behavioral responses
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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
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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
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PMID-18262282[0] A combined wireless neural stimulating and recording system for study of pain processing
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PMID-5941514[0] Feeding induced in cats by electrical stimulation of the brain stem.
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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-20404313[0] Spinal cord stimulation failed to relieve akinesia or restore locomotion in Parkinson disease.
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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
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PMID-19564605[0] Cognitive fitness of cost-efficient brain functional networks.
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{960} |
ref: -0
tags: M1 Evarts PTN conduction velocity monkey electrophysiology spinal cord
date: 12-25-2011 04:25 gmt
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PMID-14283057 Relation of Discharge Frequency to conduction velocity in pyramidal tract neurons
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PMID-11017160 Reply to One motor cortex, two different views | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{945} |
ref: -0
tags: Georgopoulos population vector arm motor control
date: 12-20-2011 22:26 gmt
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PMID-3139485 Neural integration of movement: role of motor cortex in reaching.
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PMID-16835359[0] Direct and indirect activation of cortical neurons by electrical microstimulation.
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{939} |
ref: -0
tags: Georgopoulos 1988 population vector tuning
date: 12-20-2011 01:13 gmt
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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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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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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
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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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{832} |
ref: Jimbo-2003.02
tags: MEA microstimulation artifact supression
date: 12-17-2011 01:41 gmt
revision:2
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PMID-12665038[0] A system for MEA-based multisite stimulation.
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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) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{529} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
quote: All the loose speculation provoked by roborats is ironic considering that the experiment is just a small-scale replay of a major media event that is 40 years old. In 1964, José Delgado, a neuroscientist from Yale University, stood in a Spanish bullring as a bull with a radio-equipped array of electrodes, or "stimoceiver," implanted in its brain charged toward him. When Delgado pushed a button on a radio transmitter he was holding, the bull stopped in its tracks. Delgado pushed another button, and the bull obediently turned to the right and trotted away. The New York Times hailed the event as "probably the most spectacular demonstration ever performed of the deliberate modification of animal behavior through external control of the brain." | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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PMID-19386759[0] Wireless neural stimulation in freely behaving small animals.
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PMID-17057705 Long-term motor cortex plasticity induced by an electronic neural implant.
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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
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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-16928796[0] Fast modulation of prefrontal cortex activity by basal forebrain noncholinergic neuronal ensembles in the author's own words:
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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
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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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PMID-15022843[0] A simulation study of information transmission by multi-unit microelectrode recordings key idea:
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PMID-8670641[0] The hippocampo-neocortical dialogue.
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PMID-12433288[0] Real-time computing without stable states: a new framework for neural computation based on perturbations.
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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-6457106 Processing visual feedback information for movement control.
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-- So cool! How do you come up with something like this? | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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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{896} |
ref: Friston-2002.1
tags: neuroscience philosophy feedback top-down sensory integration inference
date: 10-25-2011 23:24 gmt
revision:0
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PMID-12450490 Functional integration and inference in the brain
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I love this short story. It tastes like the complexity and frustrating richness of an examined life. It smells like the disparity between being able to understand things and being able to affect things in our incidental, stratified, limited world. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{885} |
ref: -0
tags: entropy life proteonomics transcription factors
date: 07-08-2011 22:42 gmt
revision:0
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Reduction in Structural Disorder and Functional Complexity in the Thermal Adaptation of Prokaryotes -- read the article. These are my disordered, mesothermophylic notes.
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Conjecture: the entropic cost of simulating a system (complex evolved or not) in a system (e.g. machine) versus physical reality is proportional to the information content of original system * energy per bit of simulation * time to simulate / KL divergence between simulation and reality, where information content KL divergence are calculated within the space of measured dimensions. Am I missing terms? Or rather: what terms am I missing? :-) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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http://www.chapel.duke.edu/documents/Feb13ChoosingLifeandLivingwithChoices.pdf This is a good sermon with a lot of good points (gasp! did an atheist just say that?), and is making me think hard -- or at least hard enough to write down how I feel on abortion. I agree with the pastor that the community does seem at fault for not helping women whose 'unwanted' pregnancies might be wanted if there was more social support. I guess, atheist that I am, that religion has a strong and good role here by providing the impetus to make people be more cohesive and behave morally better. IMHO Thoreau-inspired mental integrity and thoughtful examination are another (better?) way to get there, but perhaps religion is a cultural shortcut to the same end, hence valuable. (As an aside, I'm irked by the repeated phrase that 'they need the church'. Seems like the church is the needy one here - if what it offers is valuable, it need not insist. A second irritation is that the church sometimes misappropriates credit/importance: things that a community of good people create (e.g. support for single mothers) the church assumes primary credit for. You preach humility, show some.) I doubt women abort have children out of shame, as he suggests. More likely the mother understands the complex economies and timing of raising a child; presumably, in situations where an abortion is considered the mother could hope to have another child later, when she could support and raise he/she better - give he/she a happier life with more love to share to others. Are lives exchangeable in this way? You make these choices implicitly every time you choose not to have unprotected sex with someone - exchanging the possibility of creating a life presently with the possibility of creating an even better life later. This is normal and good, the point of decision has just been moved albeit utilitarian ... and of course utilitarianism is limited, because things aren't linear or monotonic. If we treat even unwanted pregnancy with 'mischievous joy' (i like that) as he suggests, perhaps the attitude of sacred life itself guarantees a happier existence than the attitude of exchangeable life. More basically: despite the logic above I intuitively and instinctively find the thought of killing anything remotely human horrific. But, we kill things all the time. We kill (are killing..) solders in war. We kill a lot of pigs, which (this is a very iffy argument here) have as much if not more capability for suffering and pleasure than a very young fetus. And then we eat them. We, loosely and implicitly, kill people by not sending an section of our income to buy medicine, food - this to grown people with families, friends and standing importance. Is their death worse than the death of a fetus? My brain tells me that there is a continuum of existence and meaning, and in this messy realistic world, we have to admit ordering and make compromises; my soul still hates this fact. Ultimately, human happiness and suffering, life and death, cannot be completely reduced to a utilitarian calculus. In such a calculus we should aim to optimize the total joy in the world, minus the pain and dread, integrated over lifetimes and people. To a rough and imperfect scale that is what we do, in our personal lives and more broadly. Treating all human lives as equal is an idea of both democracy and religion that makes the calculus balanced and fair, and allows us to derive usable laws and stable societies (integrated joy...), but it seems to break down in the case of abortion. This is why I think the oft myopic and special-interest swayed government should have no say in a woman's choice; if I can't come up with a solid reason either way, why should the government? | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Email received January 30 2011 - I've tried to understand the story, but all i get a sense of is strange injured exaggerations, statements which try to draw ire. The below-linked report is one of the most bizarre cases we have reported in quite a while. A high-paid, prestigious professor at the Catholic Duke University, who was recently appointed to the Vatican's Academy of Sciences, mind you, is not only pro abortion and in favor of gay marriage, but he also is threatening to call the police on people who disagree with him on these issues -- specifically, one pastor (Julio Severo, a friend of ours) who works with troubled homosexuals desiring to quit the lifestyle, and another Christian who has spoken only in the mildest terms of this professor. Now picture this if you will: Here we have a homeless Pastor, Julio Severo, who has been banned from his homeland for sincerely practicing his life work of helping homosexuals cope with unwanted desires. These are poor men who are terrified of contracting AIDS and dying. Some already have tested HIV positive. They come to Julio out of desperation for psychological and spiritual assistance, guidance from the Word of God. And now, for the crime of heeding God's call to help them, Pastor Severo is forced to leave his country or be jailed for the "crime" of helping homosexuals desirous of raising themselves out of a situation they deplore and fear. Because Brazil is in the hands of leftist elitists who have outlawed any public speech unfavorable to homosexuality. Meanwhile, the desolate Pastor Severo is living in poverty with a wife and children, desperately trying to fend for himself and find work to buy food for his family. All for the crime of lending people a helping hand as he felt called to do by his Savior. And now comes an elitist, high-paid, pampered professor from a prestigious university surrounded by luxury, enjoying all the finer things of life and going to the finest restaurants, working ridiculously easy hours, with lots of leisure, time and money to travel. And what does this wealthy, influential professor do? Does he offer to help the poor man who has been persecuted unjustly by a godless, heartless government for the "crime" of helping others? Why no, the professor simpers and whimpers that he is afraid of the homeless pastor who is out of work and has a hungry family with nowhere to go and no one to help them. And in an interview with a like-minded elitist blogger, this rich, comfortable professor who has all the comforts one can imagine -- things the poor pastor never even has dreamed of, brags of the high security at his lab and says that if he ever feels 'threatened' by this unfortunate penniless, harmless pastor, living in poverty and hiding somewhere in an unknown part of the world, why he will just call the police from the comfort of his luxurious home or his multi-million dollar fully equipped lab and they will arrest the homeless man so that he, the rich, pampered, powerful professor, can be even more comfortable surrounded by his art collection, his latest model car, his designer clothes, his plasma TV and his well-stocked wine cellar. Here are the details: http://laiglesforum.com/pro-abort-pro-gay-marriage-duke-u-prof-threatens-to-sic-cops-on-detractors/2091.htm Please take the time to call or email Dr. Nicolelis and ask him why he is threatening a homeless pastor for adhering to the Bible in his beliefs? And why does he think that homosexuals should be obliged to remain in their dangerous lifestyle? Does he not know that many want to quit this lifestyle for health and safety reasons? You may also ask the good doctor whether he would like to report me too for the "crime" of speaking out against this outrage. Thank you and God bless. Don Hank | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{866} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Remembered fragment of a dream, letter never sent. Probably it was ill-fated to ---. Eh. That didn't distract me from having a batshit insane dream last night, feat three army goons, myself, and the vivacious ---. The goons and I were soldiers in charge of some truck-mounted machine gun in a logged and dried-mud hillcountry - probably Serbia - but whenever we tried to get it ready, the part in question would instantly transform into crochet. When the ammunition roll was finally in the yarn gun, all the bullets would fall on the ground; look away and it would resume metallic reality. On cue to amplify our disorganization a officer sped in to harass us in a Honda S2000. Annoyed, I threw gravel at him; it transformed into a lamborghini, then a porsche, a tesla, a toyota. (I've seen a lot of fancy cars this past week). Given the frustrated Daliesque nature of the battlefield, we retreated, I as a restrained but irked soldier. Montage in, montage out, you enter to this scene as an bit-played angry estranged woman, pissed at my lack of caring / perceived neglect. Bitch complain threaten whine, I guilt but look outside and the vegetation is gorgeous, lush, the sky a mild hazy blue. I am a little thirsty. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{864} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Interesting ideas from __This Will Change Everything__
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{862} |
ref: -0
tags: backpropagation cascade correlation neural networks
date: 12-20-2010 06:28 gmt
revision:1
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The Cascade-Correlation Learning Architecture
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{860} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
"The Truth Wears Off" by Jonah Lehrer, the New Yorker.
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{859} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning by Playing: Video Games in the Classroom
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{858} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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{856} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{855} |
ref: -0
tags: sciences artificial Simon organizations economic rationality
date: 12-01-2010 07:33 gmt
revision:2
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These are notes from reading Herbert A. Simon’s The Sciences of the Artificial, third edition, 1996 (though most of the material seems from the 70s). They are half quoted / half paraphrased (as needed when the original phrasing was clunky). I’ve added a few of my own observations, and reordered the ideas from the book. “A large body of evidence shows that human choices are not consistent and transitive, as they would be if a utility function existed ... In general a large gain along one axis is required to compensate for a small loss along another.†HA Simon. Companies within a capitalist economy make almost negligible use of markets in their internal functioning†- HA Simon. Eg. they are internally command economies. (later, p 40...) We take the frequent movability and indefiniteness of organizational boundaries as evidence that there is often a near balance between the advantages of markets and organizationsâ€
That said: organizations are not highly centralized structures in which all the important decisions are made at the center; this would exceed the limits of procedural rationality and lose many of the advantages attainable from the use of hierarchical authority. Business organizations, like markets, are vast distributed computers whose decision processes are substantially decentralized. In fact, the work of the head of a corporation is a market-like activity: allocating capital to promising or desirable projects. In organizations, uncertainty is often a good reason to shift from markets to hierarchies in making decisions. If two different arms of a corporation - production and marketing - make different decisions on the uncertain number of units to be sold next year, there will be a problem. It is better for the management to share assumptions. “Left to the market, this kind of uncertainty leads directly to the dilemmas of rationality that we described earlier in terms of game theory and rational expectations†I retain vivid memories of the astonishment and disbelief expressed by the architecture students to whom I taught urban land economics many years ago when I pointed to medieval cities as marveluosly patterned systems that had mostly just ‘grown’ in response to myriads of individual human decisions. To my students a pattern implied a planner in whose mind it had been conceived and whose hand it had been implemented. The idea that a city could acquire its patter as naturally as a snowflake was foreign to them ... they reacted to it as many christian fundamentalists responded to Darwin: no design without a Designer! Markets appear to conserve information and calculation by assigning decisions to actors who can make them on the basis of information that is available to them locally. von Hayek: “The most significant fact about this system is the economy of knowledge with which it operates, o how little the individual participants need to know in order to make the right actionâ€. To maintain actual Pareto optimality in the markets would require information and computational requirements that are exceedingly burdensome and unrealistic (from The New Palgrave: A dictionary of Economics) Nelson and winter observe that in economic evolution, in contract to biological evolution, sucessful algorithms (business practices) may be borrowed from one firm to the other. The hypothesized system is Lamarkian, because any new idea can be incorporated in opearting procedures as soon as its success is observed" . Also, it's good as corporations don't have secual reproduction / crossover. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{844} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
"Stage 6" part selection:
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{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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{815} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{826} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data Scargle, J. D.
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{825} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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{821} |
ref: work-0
tags: differential evolution function optimization
date: 07-09-2010 14:46 gmt
revision:3
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Differential evolution (DE) is an optimization method, somewhat like Neidler-Mead or simulated annealing (SA). Much like genetic algorithms, it utilizes a population of solutions and selection to explore and optimize the objective function. However, it instead of perturbing vectors randomly or greedily descending the objective function gradient, it uses the difference between individual population vectors to update hypothetical solutions. See below for an illustration. At my rather cursory reading, this serves to adapt the distribution of hypothetical solutions (or population of solutions, to use the evolutionary term) to the structure of the underlying function to be optimized. Judging from images/821_1.pdf Price and Storn (the inventors), DE works in situations where simulated annealing (which I am using presently, in the robot vision system) fails, and is applicable to higher-dimensional problems than simplex methods or SA. The paper tests DE on 100 dimensional problems, and it is able to solve these with on the order of 50k function evaluations. Furthermore, they show that it finds function extrema quicker than stochastic differential equations (SDE, alas from 85) which uses the gradient of the function to be optimized. I'm surprised that this method slipped under my radar for so long - why hasn't anyone mentioned this? Is it because it has no proofs of convergence? has it more recently been superseded? (the paper is from 1997). Yet, I'm pleased because it means that there are also many other algorithms equally clever and novel (and simple?), out their in the literature or waiting to be discovered. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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 ;-) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{808} |
ref: Inzlicht-2009.03
tags: uncertainty religion conviction decision science
date: 02-02-2010 20:39 gmt
revision:3
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The Neural Markers of Religious Conviction PMID-19291205 Recently a friend pointed this article out to me, and while I found the scientific results interesting though slightly questionable - that religious people have less anterior cingulate cortex activation upon error - the introduction and discussion were stimulating. What follows are a few quotes and my interpretation and implications of the authors' viewpoint. "The absence of a cognitive map providing clear standards and goals is uncomfortable and leads people to search for and assert belief systems that quell their anxiety by allowing for clearer goal pursuit (McGregor, Zanna, Holmes, & Spencer, 2001)." I would argue that uncertainty itself is highly uncomfortable - whether it is uncertainty as to how much food you will have in the future, or uncertainty as to the best behavior. In this sense, of course religion decreases anxiety - it provides a structured way to think about this disordered and highly undecidable world, a filter to remove or explain away many of the random parts of our lives. In my personal experience, conviction is usually easier than trying to hold accurate probabalistic models in your mind - conviction is pleasurable, even if it is wrong. I find their short review of cognitive science in the introduction interesting - they claim that the septo-hippocampal system is concerned with the detection and correction of errors associated with concrete behaviors and goals, while in humans (and other primates?) the ACC allows error and feedback based operations on concepts and higher-order goals. The need for a higher-level error detection circuit makes sense in humans, as we are able to bootstrap our behavior to very complicated limits, but it also begs to question - what trains the ACC? To some degree, it must train itself in the via the typical loopy feedback-based brain way, but this only goes so far, as (at least in the modern world) the space of all possible behaviors, longterm and short term, given stochastic feedback is too large to be either decidable or fully parseable/generalizable into an accurate global model, even given a lifetime of experience. Religion, as this paper and many others posits, provides this global model against which behaviors and perceptions can be measured. But why does a uncertainty challenge causes a compensatory increase in the strength of convictions, almost to the point of zealousness (how is this adaptive? just as a means of reducing anxiety?); I've seen it happen, but why. From a Bayesian point of view, increased uncertainty necessitates decreased certainty, or fewer convictions. From a pragmatic point of view, increased uncertainty requires increased convictions purely because the convictions have to make up for the lack of environmental information from which to make a decision. Any theory must include the cost of not making a decision, the cost of delaying a decision, and the principle of sunk costs. There are other solutions to the 'undecidable' problems of life than religion - literary culture and science come to mind. The principle behind all may be that, while individual experience and intellect is possibly insufficient for generating global rules to guide behavior, the condensed experience of thousands/millions/billions of people is. This assumes that experience, as a random variable/signal, scales according to the laws of large numbers - noise decreases monotonically as sample size increases. This may not actually be true, it depends on the structure of the distributions, and the extent to which people's decisions/behaviors are orthogonal, and the fidelity of the communication / aggregation channels which operate on the data. I think the dimensionality increase afforded by larger sample size is slower than the concomitant noise decrease, hence (valid) global rules guiding behavior can be extracted from large populations of people. Regarding the communication channels, it seems there were always high fidelity channels of experience - e.g. Homer, Benjamin Franklin's transatlatic trips, the royal Society of London, (forgive my western pov) - and now, there are even more (the internet)! The latter invention should, at least within the framework here, allow larger groups of people to make 'harder' or 'more undecidable' decisions by virtue of greater information. Fairly standard rhetoric to the internet crowd (c.f. forums), I know. I would argue that this is better than using convictions... but the result of communication / aggregation is convictions anyway, so eh. Getting back to the uncertainty issue, the authors point out that conservative cultures there is usually greater uncertainty (which way is the arrow of causality?), and increasing uncertainty bolsters support zealous action, e.g. war. "For example, contemporary social psychological research indicates that uncertainty threats can cause people to become more extreme in their opinions, so that they exaggerate their religious convictions and become more willing to support a war to defend those convictions (McGregor, Haji, Nash, & Teper, 2008). In fact, even nonbelievers bolster their personal convictions to near-religious levels in order to reduce uncertainty-related distress (McGregor et al., 2001). Thus, in terms of feedback-loop models, the standards and predictions provided by religious convictions are strong enough that they can resist any discrepant feedback that might alert the comparator system." This, I believe, is fairly accurate, and it implies several dramatic things: if a despot or leader wishes to engender support for a war, particularly a religious war, then he should make the lives of his constituents uncertain. If their lives are stable and certain sans ideology, then they will be less likely to have the convictions ('the other side is bad!') to fight certain wars. (It of course depends on who/what the other side is!). Take Europe vs. America as an example - America has far fewer social support systems and greater uncertainty in life than in Europe. The Economist frequently phrases American businesses' penchant for hiring and firing people quickly and seemingly at whim, as it encourages creative reuse, economic flexibility, and better allocation of capital, but it has a clear downside - increased anxiety, uncertainty. We (well, not me, but many Americans) deal with this via religion, the article would argue (that said, I should guess that there are a great many other reasons people are religious). Still, in western Europe has less uncertainty in life, is more secular, and less tolerant of ideological wars. Hence the antidote for war is to give people stable, significant lives. More common-sense rhetoric. On to another suggestive point made by the article: "In terms of feedback-loop models, this explanation suggests that the standards and predictions provided by religion are inadequate and should, in fact, result in prediction errors; however, because religious beliefs are rigid, inconsistent information is reinterpreted in such a way that it becomes assimilated to preexisting convictions, further sustaining beliefs (Park, 2005)." I would be interested in an actual test of this hypothesis - if it is possible without bias (perhaps another EEG study? perhaps it has been already done?) The authors actually prove the opposite point, that religions people are more likely to answer correctly on the Stroop test. They take more time, but seem to be more careful. This reminds me of Matteo Ricci, who allegedly used his Jesuit training in sustained concentration and memorization to master the Chinese language; clearly religion is far more than just a means of reducing perceived uncertainty about the world. To loop the argument back on its tail - this is the 'meta' blog, afterall - one may question if the theory (looking at behavior in terms of the unpleasantness of uncertainty and the need for decidability) is a good way of looking at things, just as we questioned if religion is a good theory of the world. I think it generalizes; for example, Solaiman mentioned that the European children of the revolution of 1968 had parents who notably applied very little guidance to their lives; they were like the American hippies. These people grew up disliking their parents, and sought far more structure in their lives and in parenting their own children. One may imagine that they disliked the vast uncertainty their parents bluntly exposed them to, and paucity of guiding principles - something that the parents, after years of living in the world, probably had. Secondly, Solaiman recalled that all his favorite teachers were those that were strictest, strongest in their conviction, and most structured in their pedagogy. People seek to make decisions decidable whether through parents, teachers, religion, science or even art and literature. To summarize, uncertainty engenders convictions by the pragmatic principle. Best thing we can do is to either reduce uncertainty or found those convictions on aggregate data(*) (*) Google publication. The principle of data is our zeitgist, but history suggests that independent of what we think now it will not be the last. comments? edit this, write below. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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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{786} |
ref: -0
tags: linux keyboard international characters symbols
date: 10-01-2009 14:09 gmt
revision:1
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Need to type international symbols and characters on your keyboard, e.g. for writing in another language? Do this: cp /usr/share/X11/locale/en_US.UTF-8/Compose ~/.XCompose xmodmap -e 'keycode 115 = Multi_key Multi_key Multi_key Multi_key' xmodmap -e 'keycode 116 = Multi_key Multi_key Multi_key Multi_key' Where 115 and 116 are the windows keys on my keyboard. (You can find this out for your keyboard by running 'xev'); Then:
yay! And now for something completely unrelated but highly amusing, at least in title: Optimal Brain Damage | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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! | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{767} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
to read!
to think!
to make!
to program!
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{766} |
ref: life-0
tags: enfranchised mind blog population wealth future
date: 07-06-2009 21:43 gmt
revision:0
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http://enfranchisedmind.com/blog/posts/the-fermi-paradox/
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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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{756} |
ref: life-0
tags: education wikinomics internet age college university pedagogy
date: 06-11-2009 12:52 gmt
revision:0
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Will universities stay relevant? and the rest of the wikinomics blog
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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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http://www.theatlantic.com/doc/200501/kirn -- goood.
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PMID-15286181[0] Providing explicit information disrupts implicit motor learning after basal ganglia stroke.
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For the great enemy of truth is very often not the lie—deliberate, contrived, and dishonest—but the myth— persistent, persuasive, and unrealistic. Too often we hold fast to the clichés of our forbears. We subject all facts to a prefabricated set of interpretations. We enjoy the comfort of opinion without the discomfort of thought. —John F. Kennedy | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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{716} |
ref: Ribeiro-2004.12
tags: Sidarta Ribeiro reverberation sleep consolidation integration replay REM SWS
date: 03-26-2009 03:19 gmt
revision:2
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PMID-15576886[0] Reverberation, storage, and postsynaptic propagation of memories during sleep
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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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PMID-19233172[0] Synchronisation in the beta frequency-band - The bad boy of parkinsonism or an innocent bystander?
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{708} |
ref: Wagner-2004.01
tags: sleep insight mental restructure integration synthesis consolidation
date: 03-20-2009 21:31 gmt
revision:1
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PMID-14737168[0] Sleep Inspires Insight.
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the organization of the human triphasic EMG control sequence:
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PMID-16794848[9] Bilateral basal ganglia activation associated with sensorimotor adaptation.
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PMID-17855611 Motor Force Field Learning Influences Visual Processing of Target Motion
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PMID-18951924[0] Consciousness and the consolidation of motor learning
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{678} |
ref: Rasch-2009.06
tags: sleep cholinergic acetylcholine REM motor consolidation
date: 02-18-2009 17:27 gmt
revision:0
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PMID-19194375[0] "Impaired Off-Line Consolidation of Motor Memories After Combined Blockade of Cholinergic Receptors During REM Sleep-Rich Sleep."
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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
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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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{652} |
ref: notes-0
tags: policy gradient reinforcement learning aibo walk optimization
date: 12-09-2008 17:46 gmt
revision:0
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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
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PMID-17178410[0] A central source of movement variability.
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My comments on this blog post, preseved here for posterity: I agree with William’s first point, spectrum is ‘owned’ by everybody; the government’s only purpose is to regulate it so that it remains an effective communication medium. Like the bandwidth that it uses, the communication system is optimally owned by users, hence it is a bad idea to auction off segments of spectrum for exclusive use by corporations. Examine at what happened to the 2.4 GHz band, an area where water absorption is high and most households have a 1kw noise generator (microwave oven): EVERYONE USES IT because it is FREE and OPEN, no licenses required. Just look at all the innovation created for this band: 802.11, bluetooth, ZigBee, cordless phones, wireless remotes, and others. If 802.11 was in the 700-1GHz band someone or a company could easily make long-distance wireless repeaters & mesh-network nodes, sell them to consumers, and everyone could SIP for FREE without paying Verizon / ATT etc. This could set it up as a pyramid scheme, where to get on the network you simply have to buy a mesh node repeater, and with it became part of the ‘corporation’ which provided your wireless services. A certain part of the purcase & access price would, of course, need to go to pay for backbone connections, service, matenance and extending connection to remote areas, but this too can be solved and managed efficiently with something like 1 phone = 1 share. With coprotations, you either have redundancy (two networks w/ twice as many cell towers) or a monopoly; neither are economically efficient. A re-allocation of prime wireless spectrum back to the correct owners - the citizens - would spur American Innovation greatly and simultaneously cut communication costs. The technology is changing, and the policy should too! Anyway, i’m sick of paying $0.10 for 100 bytes of data (txt messages) when audio data costs ~1/500th that. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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PMID-18004384[0] A synaptic memory trace for cortical receptive field plasticity.
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PMID-9448252[0] The acquisition of skilled motor performance: Fast and slow experience-driven changes in primary motor cortex
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PMID-17237780[0] Switching from automatic to controlled action by monkey medial frontal cortex.
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PMID-8985875 Neural information transferred from the putamen to the globus pallidus during learned movement in the monkey.
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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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PMID-14511525 Probing changes in neural interaction during adaptation.
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both index finders push on the food!!! however! PMID-9751054 Primary motor cortex is involved in bimanual coordination
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PMID-17690132 Disrupted prediction-error signal in psychosis: evidence for an associative account of delusions.
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PMID-15858046[] Redundancy and Synergy of Neuronal Ensembles in Motor Cortex
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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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http://www.dana.org/news/cerebrum/detail.aspx?id=3066 -- great article, with a well thought out, delicate treatment of the ethical/moral/ legal issues created by the interaction between the biological roots of violence (or knowlege thereof) and legal / social systems. He posits that there must be a continuum between ratinoal free will and irrational, impulsive violent behavior, with people biased to both by genetics, development, traumatic head injury, and substance abuse (among others). | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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I needed to evaluate different fixed-point filters & implement them in a way which would more-or-less easily transfer to hand-coded blackfin assembly. Hence, I wrote a small program in C to test two likely alternatives: a 5th order elliptic filter, with 82db stopband rejection, or a 4th order elliptic, with 72 db stopband rejection. see {421} also, and this useful reference. (also at {584}) UPDATE: this filter is not stable/ suitable for the 1.15 signed fraction format of the blackfin processor. As in the reference, you must use a non-canonic form which puts the numerator before the denominator. I had a great deal of difficulty trying to determine why the output of the Direct Form II filter was giving crap results with poles/zeros designed for lowpass operation. The reason appears to be that the Direct FormII filter requires dynamic range > +-1 for the w's (first feedback section). The numerator / b coefficients for these highpass filters is usually [1 -2 1] - a highpass - which returns 0 when passed saturated data. See the signal flowchart below. The program for implementing these filters as cascaded biquad/ triquads follows. The method of generating the filter coefficients is in the comments. Note that the filter coefficients for the triquad exceed and absolute magnitude of 2, hence the triquad implementation has a fixed-point format of s.2.13 (one bit sign, 2 bits whole part, 13 bits fractional part). The biquad has s.1.14 format - one bit for the whole part. Also note that I incorporated a scale factor of x2 into the first stage, and x4 into the second stage, as the output of the ADC is only 12 bits and we can afford to expand it to the full range. The filter responses, as designed (in floating point). 5th order is on the left, 4th on the right. The filter output to white noise, in the time domain. 5th order has, in this impementation, a 'softer shoulder', which i do not think is appropriate for this application. The same output in fourier space, confirming the softer shoulder effect. this was actually kinda unespected .. I thought an extra pole/zero would help! I guess the triquad & lower res coefficient quantization is less efficient.. ? #include <stdio.h> // gcc -Wall filter_test.c -o filter_test //need to test fixed-point filtering in C (where it is easy) //before converting to hand-coded assembly. short w1_1[2]= {0,0}; short w1_2[3]= {0,0,0}; short w2_1[2]= {0, 0}; short w2_2[2]= {0, 0}; /* filter 1: 5th order. biquad then a triquad. [B1, A1] = ellip(5,0.2,84, 6/31.25); %84db = 14 bits. rb = roots(B1); ra = roots(A1); pa1 = poly(ra(1:2)); pa2 = poly(ra(3:5)); pb1 = poly(rb(1:2)); pb2 = poly(rb(3:5)); b1_1 = round(pb1*sqrt(B1(1))*2^15) a1_1 = round(pa1* 2^14)* -1 b1_2 = round(pb2*sqrt(B1(1))*2^15) a1_2 = round(pa2*2^13)*-1 % triquad. */ //biquad1: short b1_1[3] = {1199, 87, 1199}; short a1_1[2] = {24544 -14085}; //triquad1: short b1_2[4] = {1199, 2313, 2313, 1199}; short a1_2[3] = {18090, -14160, 3893}; /*filter 2: can be implemented by 2 biquads. [B3, A3] = ellip(4,0.5,72, 6/31.25); rb = roots(B3); ra = roots(A3); pa1 = poly(ra(1:2)); pa2 = poly(ra(3:4)); pb1 = poly(rb(1:2)); pb2 = poly(rb(3:4)); b2_1 = round(pb1*sqrt(B3(1))*2^15) a2_1 = round(pa1* 2^14)* -1 b2_2 = round(pb2*sqrt(B3(1))*2^16) %total gain = 8. a2_2 = round(pa2*2^14)*-1 */ //biquad2: short b2_1[3] = {2082, 914, 2082}; short a2_1[2] = {24338, -13537}; //biquad2 (2): short b2_2[3] = {4163, 6639, 4163}; short a2_2[2] = {24260, -9677}; int madd_sat(int acc, short a, short b){ //emulate the blackfin //signed-integer mode of operating. page 567 in the programming reference. int c; long long l, lo, hi; lo = (long long)(-2147483647); hi = (long long)(2147483647); c = (int)a * (int)b; l = (long long)acc + (long long)c; if(l < lo) l = lo; if(l > hi) l = hi; acc = (int)l; return acc; } //need to deal with samples one at a time, as they come in, from different channels //need to quantize the coeficients //need to utilize a biquad topology / filter structure. // (this is simple - just segregate the poles. order 5 filter req. biquad & triquad. short filter_biquad(short in, short* a1, short* b1, short* w1){ int acc; short out, w; //in varies from 0 to 0x0fff - ADC is straight binary. (0 to 4095). unsigned. acc = madd_sat(0, in, 16384); acc = madd_sat(acc, w1[0], a1[0]); acc = madd_sat(acc, w1[1], a1[1]); w = (short)(acc >> 14); //hopefully this does sign-extended shift. acc = madd_sat(0, w, b1[0]); acc = madd_sat(acc, w1[0], b1[1]); acc = madd_sat(acc, w1[1], b1[0]); //symmetry. out = (short)(acc >> 14); w1[1] = w1[0]; w1[0] = w; return out; } short filter_triquad(short in, short* a2, short* b2, short* w2){ int acc; short out, w; //this one is a bit different, as the coefficients are > 2 in the denom. // hence have to normalize by a different fraction. //multiply the numerator by 4 for a net gain of 8. acc = madd_sat(0 , in, 8192); acc = madd_sat(acc, w2[0], a2[0]); acc = madd_sat(acc, w2[1], a2[1]); acc = madd_sat(acc, w2[2], a2[2]); w = acc >> 13; acc = madd_sat(0 , w, b2[0]); acc = madd_sat(acc, w2[0], b2[1]); acc = madd_sat(acc, w2[1], b2[1]); acc = madd_sat(acc, w2[2], b2[0]); //symmetry. out = (short)(acc >> 13); w2[2] = w2[1]; w2[1] = w2[0]; w2[0] = w; return out; } int main( int argv, char* argc[]){ //read the samples from stdin. int in, out; while(scanf("%d", &in)){ //compare 5th and 4th order filters directly. in -= 2048; out = filter_biquad(in, a1_1, b1_1, w1_1); out = filter_triquad(out, a1_2, b1_2, w1_2); printf("%d ", out); out = filter_biquad(in, a2_1, b2_1, w2_1); out = filter_biquad(out, a2_2, b2_2, w2_2); printf("%d ", out); } return 0; } // gcc -Wall filter_test.c -o filter_test // cat filter_test_in.dat | ./filter_test > filter_test_out.dat Below, some matlab code to test the filtering. % make some noisy data. x = randn(2000, 1); x = x .* 1000; x = x + 2048; i = find(x >= 4096) x(i) = 4095; i = find(x < 0); x(i) = 0; x = round(x); fid = fopen('filter_test_in.dat', 'w'); for k = 1:length(x) fprintf(fid, '%d ', x(k)); end fprintf(fid, 'quit'); fclose(fid); Here is a blackfin assembly implementation of the filter, Note the filter weights (i0) and delays (i1, i2) need to be on two different cache/sram banks, or the processor will stall. Also note that , as per the second diagram above, the delays for biquad 1 output are synonymous to the delays for biquad 2 input, hence they are only represented once in memory. /* directform 1 biquad now, form II saturates 1.15 format. operate on the two samples in parallel (both in 1 32bit reg). r0 x(n) -- the input from the serial bus. r1 x(n-1) (yn-1) -- ping-pong the delayed registers. r2 x(n-2) (yn-2) -- do this so save read cycles. r3 y(n-1) (xn-1) r4 y(n-2) (xn-2) r5 b0 b1 -- (low high) r6 a0 a1 i0 reads the coeficients into the registers; it loops every 32 bytes (16 coef, 4 biquads) i1 reads the delays. it loops every 640 bytes = 10 delays * 4 bytes/delay * 16 stereo channels. only increments. i2 writes the delays, loops every 640 bytes. also only increments. if i1 and i2 are dereferenced in the same cycle, the processor will stall -- each of the 1k SRAM memory banks has only one port. format of delays in memory: [x1(n-1) , x1(n-2) , x2(n-1) aka y1(n-1) , x2(n-2) aka y1(n-2) , x3(n-1) aka y2(n-1) , x3(n-2) aka y2(n-2) , x4(n-1) aka y3(n-1) , x4(n-2) aka y3(n-2) , y4(n-1) , y4(n-2) ] --that's 10 delays, 4 bytes each. */ r5 = [i0++] || r1 = [i1++]; a0 = r0.l * r5.l , a1 = r0.h * r5.l || r6 = [i0++] || [i2++] = r0; a0 += r1.l * r5.h, a1 += r1.h * r5.h || r2 = [i1++] ; a0 += r2.l * r5.l, a1 += r2.h * r5.l || r3 = [i1++] ; a0 += r3.l * r6.l, a1 += r3.h * r6.l || r4 = [i1++] ; r0.l = (a0 += r4.l * r6.h), r0.h = (a1 += r4.h * r6.h) (s2rnd) || [i2++] = r1; r5 = [i0++] || [i2++] = r0; a0 = r0.l * r5.l, a1 += r0.h * r5.l || r6 = [i0++] || [i2++] = r3; a0 += r3.l * r5.h, a1 += r3.h * r5.h || r1 = [i1++]; a0 += r4.l * r5.l, a1 += r4.h * r5.l || r2 = [i1++]; a0 += r1.l * r6.l, a1 += r1.h * r6.l; r0.l = (a0 += r2.l * r6.h), r0.h = (a1 += r2.h * r6.h) (s2rnd); r5 = [i0++] || [i2++] = r0; a0 = r0.l * r5.l, a1 = r0.h * r5.l || r6 = [i0++] || [i2++] = r1; a0 += r1.l * r5.h, a1 += r1.h * r5.h || r3 = [i1++]; a0 += r2.l * r5.l, a1 += r2.h * r5.l || r4 = [i1++]; a0 += r3.l * r6.l, a1 += r3.h * r6.l; r0.l = (a0 += r4.l * r6.h), r0.h = (a1 += r4.h * r6.h) (s2rnd); r5 = [i0++] || [i2++] = r0; a0 = r0.l * r5.l, a1 += r0.h * r5.l || r6 = [i0++] || [i2++] = r3; a0 += r3.l * r5.h, a1 += r3.h * r5.h || r1 = [i1++]; a0 += r4.l * r5.l, a1 += r4.h * r5.l || r2 = [i1++]; a0 += r1.l * r6.l, a1 += r1.h * r6.l; r0.l = (a0 += r2.l * r6.h), r0.h = (a1 += r2.h * r6.h) (s2rnd); [i2++] = r0; //save the delays. [i2++] = r1; //normally this would be pipelined. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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/ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{565} |
ref: Walker-2005.12
tags: algae transfection transformation protein synthesis bioreactor
date: 03-21-2008 17:22 gmt
revision:1
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Microalgae as bioreactors PMID-16136314
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{551} |
ref: notes-0
tags: DNA transfection yasuda experiment8
date: 03-17-2008 20:11 gmt
revision:2
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"
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{546} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
http://www.sgvsarc.com/demo.htm
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{545} |
ref: notes-0
tags: telecommunications FCC wireless regulation government
date: 02-26-2008 04:18 gmt
revision:2
[1] [0] [head]
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http://news.zdnet.com/2010-1035_22-6231729.html
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{539} |
ref: -0
tags: laser power concentration GFP mCherry calibration
date: 02-01-2008 19:22 gmt
revision:0
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above, a set of curves for determining fluorescent protein concentration (GFP & mCherry) from received photon count in a two-photon microscope. Unfortunately, these depend on efficiency & power of the entire setup, so the curve is non-transferable to other microscopes. one pass of mCherry @ 5x dilution did not seem the same as the others -- perhaps the reading light was left on? % given a series of files, % calculate a quadratic to convert intensity to concentration. % assumed formula: % green intensity = background + const*[GFP]*laserpower^2 % red intensity = background + const*[RFP]*laserpower^2 + % const2[GFP]*laserpower^2 close all cd('/var/ftp/tim_hanson/T002-0130_08/solutions'); basename = 'T002-gfp-100xdil-'; int_gfp100 = IntensReadfile('T002-gfp-100xdil-', 11, 2); int_gfp10 = IntensReadfile('T002-gfp-10xdil-', 8, 2); int_mcherry10 = IntensReadfile('T002-mcherry-10xdil-', 7, 2); int_mcherry5 = IntensReadfile('T002-mcherry-5xdil-', 7, 2); int_mcherry5_2 = IntensReadfile('T002-mcherry-5xdil2-', 7, 2); int_mcherry5_4 = IntensReadfile('T002-mcherry-5xdil4-', 6, 2); bg_green = (int_gfp100(1) + int_gfp10(1))/2; bg_red = (int_mcherry10(1) + int_mcherry5(1)... + int_mcherry5_2(1) + int_mcherry5_4(1))/4; powers = (0:0.1:1).^2; int_gfp_all = [int_gfp100-bg_green, (int_gfp10-bg_green)/10]; pow_gfp_all = [powers(1:11), powers(1:8)]; green_intensity_perpower = pow_gfp_all'\int_gfp_all' green_lab = ['green intensity = ' num2str(green_intensity_perpower) ' * power^2 + ' ... num2str(bg_green) ' (photons/10us) @ 8.7 ug/ml conc. gfp']; figure plot(sqrt(powers(1:11)), int_gfp100, 'o'); hold on plot(sqrt(powers(1:8)), (int_gfp10-bg_green)/10+bg_green, 'or'); plot(sqrt(pow_gfp_all), pow_gfp_all * green_intensity_perpower + bg_green, 'gx'); legend('100x dilution','10x dilution','parabolic fit'); title('intensity of gfp vs. laser power normalized to 100x dilution') xlabel(green_lab); int_mch_all = [(int_mcherry10-bg_red)/10, (int_mcherry5-bg_red)/20, ... (int_mcherry5_2-bg_red)/20, (int_mcherry5_4-bg_red)/20]; pow_mch_all = [powers(1:7), powers(1:7), powers(1:6), powers(1:7)]; red_intensity_perpower = pow_mch_all'\int_mch_all' red_lab = ['red intensity = ' num2str(red_intensity_perpower) ' * power^2 + ' ... num2str(bg_red) ' (photons/10us) @ 8.7 ug/ml conc. mcherry']; figure plot(sqrt(powers(1:7)), (int_mcherry10-bg_red)/10+bg_red, 'o'); hold on plot(sqrt(powers(1:7)), (int_mcherry5-bg_red)/20+bg_red, 'or'); plot(sqrt(powers(1:7)), (int_mcherry5_2-bg_red)/20+bg_red, 'ok'); plot(sqrt(powers(1:6)), (int_mcherry5_4-bg_red)/20+bg_red, 'om'); plot(sqrt(pow_mch_all), pow_mch_all * red_intensity_perpower + bg_red, 'gx'); legend('10x dilution','5x dilution','5x dilution(2)','5x dilution(4)','parabolic fit'); title('intensity of mcherry vs. laser power normalized to 100x dilution') xlabel(red_lab) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{530} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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{532} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
http://www.biotele.com/Delgado.htm
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{528} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{527} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
http://wonko.com/article/554 -- niice :) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{526} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
I'm posting my comments about http://flock.com/ here just in case they are removed from the actual site This is all very interesting. I just downloaded it, and flock seems to work well. I'm probably not going to use it unless there is some demonstrable technical superiority (e.g. leaks less memory than firefox), as the social sites just distract me from getting work done. Anyway, I have a question: how are you going to make money? How are you paying the developers? If you are not and it is all OSS, where is the source? It seems like the VC's are just throwing money away for the (hypothetical) good of the social-network crowd. Or, rather, you are indirectly funding the popularity of sites that flock makes it easy to get at. Are these sites (e.g. facebook) paying you? Wait -- flock allows you to look at content and not the ads. They are not paying you. Perhaps you are moving along the lines of Opera, and intending to get people addicted to flock to a degree that they demand it on their mobile devices. Mobile devices are closed (for now .. check google), hence you can make money licensing software to phone manufacturers. I imagine that you'll have to rewrite the Mozilla core to do this (unless phones become significantly more powerful - not likely, they are battery devices. ) Mozilla is (L)GPL - you'll have to release the source. To the best of my knowledge, with non-physical goods money can only be made from gradients in knowledge (pun.. intended), therefore you will have to keep the source closed. If this is the case, you'll be able to make money (on this, i don't know what else you have planned) for a while, and when you can no longer, I hope you open the source like netscape. Technically, though, excellent job! your website is also very pretty! | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{522} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
from http://www.fcc.gov/oet/info/rules/part15/part15-9-20-07.pdf :
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{503} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
quote: Consumers also pay high taxes for telecommunication services, averaging about 13 percent on some telecom services, similar to the tax rate on tobacco and alcohol, Mehlman said. One tax on telecom service has remained in place since the 1898 Spanish-American War, when few U.S. residents had telephones, he noted. "We think it's a mistake to treat telecom like a luxury and tax it like a sin," he said. from: The internet could run out of capacity in two years comments:
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{493} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-17614134[0] Equalization filters for multiple-channel electromyogram arrays.
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{483} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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{479} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
http://pespmc1.vub.ac.be/books/IntroCyb.pdf -- dated, but still interesting, useful, a book in and of itself!
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{378} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
http://www.palowireless.com/infotooth/tutorial/baseband.asp
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{476} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{464} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The problem: I have an interrupt status routine (ISR) which can interrupt the main, radio-servicing routine at any time. To keep the ISR from corrupting the register values of the main routine while it works, these registers must be pushed, and later popped, to the stack. Now, doing this takes time, so I'd prefer to pop / push as few registers as possible. Namely, I don't want to push/pop the hardware loop registers - LC0 (loop counter 0), LB0 (loop bottom 0, where the hardware loop starts) & LT0 (loop top 0, where the hardware loop ends). Gcc seems to only touch bank 1, never bank 0, so I don't have to save the 3 regs above. However, to make sure, I've written a perl file to examine the assembled code: my $file = "decompile.asm"; open(FH, $file); @j = <FH>; my $i=0; my @badregs = ("LC0", "LB0", "LT0"); foreach $reg (@badregs){ foreach $k (@j){ if($k =~ /$reg/){ $i++; print "touch register $reg : $k"; } } } #tell make if we found problems or not. if($i>0){ exit 1; }else{ exit 0; } 'make' looks at the return value perl outputs, as instructed via the makefile (relevant portion below): headstage.ldr:headstage.dxe rm -f *.ldr $(LDR) -T BF532 -c headstage.ldr $< bfin-elf-objdump -d headstage.dxe > decompile.asm perl register_check.pl if it finds assembly which accesses the 'bad' registers, make fails. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{468} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
On the 'headstage' I'm developing, there is a feild in the status packet for version number, so i wanted to automatically include the SVN version into this field by updating the source code. Rather than fiddling around with the SVN settings, I used perl (as always). # script to insert the SVN version.. $verstr = `svn info`; if($verstr =~ /Revision: (\d+)/ ){ $version = $1; # print "svn version: $version \n"; open(FH, "main.c"); open(FHnew, ">main.c.new"); #open in write-over mode. @j = <FH>; foreach $line (@j){ $line =~ s/(SVN_VERSION\{\*\/)([^\/]*)(\/\*\})/$1$version$3/; print FHnew $line; } close FH; close FHnew; `mv main.c.new main.c`; }lovely regex, no? it replaces: stat->version = /*SVN_VERSION{*//*}*/;with stat->version = /*SVN_VERSION{*/59/*}*/;(for example) in the file main.c (obviously) Mr tlh24, I suggest the following: Add the string "$Rev$" to your main.c $ svn ps svn:keywords "Rev" main.c $ svn ci -m "adding Rev keyword" main.c Now, eg: stat->verstr = "$Rev$";becomes stat->verstr = "$Rev: 59$"; cheers, /joeyo Mr. joeyo: ahhh, that's sweet! problem is: i need it formatted as an integer. stat->version is type 'unsigned int'. any ideas? | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Self-learning fuzzy neural network with optimal on-line leaning for water injection control of a turbocharged automobile.
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{463} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
http://www.ntlf.com/html/lib/quotes.htm
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{450} |
ref: notes-0
tags: leadership dilbert redirection politics trolls
date: 09-28-2007 18:11 gmt
revision:0
[head]
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a very nice synopsis of how leadership works: | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{409} |
ref: bookmark-0
tags: optimization function search matlab linear nonlinear programming
date: 08-09-2007 02:21 gmt
revision:0
[head]
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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
[head]
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http://www.johanforrer.net/BLACKFIN/index.html very good, very instructive. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{401} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
http://hardm.ath.cx:88/pdf/RFpenetrationInTissue.pdf
even more interesting: wireless brain machine interface | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{367} |
ref: notes-0
tags: RF telemetry differential phase shift key prosthesis power transmission TETS PSK
date: 05-12-2007 23:13 gmt
revision:0
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transcutaneous data telemetry system tolerant to power telemetry interference
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{360} |
ref: thesis-0
tags: clementine 042607 operant conditioning
date: 04-27-2007 16:45 gmt
revision:3
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{80} |
ref: Chan-2006.12
tags: computational model primate arm musculoskeletal motor_control Moran
date: 04-09-2007 22:35 gmt
revision:1
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PMID-17124337[0] Computational Model of a Primate Arm: from hand position to joint angles, joint torques, and muscle forces ideas:
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{344} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-2027042[0] Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets.
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{294} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-2376768[0] Making arm movements within different parts of space: dynamic aspects in the primate motor cortex
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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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{340} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-12522173[0] Systematic changes in motor cortex cell activity with arm posture during directional isometric force generation.
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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
[1] [0] [head]
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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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{326} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-9331494[0] Force and the motor cortex.
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{302} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-17271333[0] Neuron selection and visual training for population vector based cortical control.
PMID-16705272[1] Selection and parameterization of cortical neurons for neuroprosthetic control
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{258} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-17271178[0] automatic spike sorting for neural decoding
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{296} |
ref: Kettner-1988.08
tags: 3D motor control population_vector Schwartz Georgopoulos
date: 04-05-2007 17:09 gmt
revision:1
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A triptych of papers (good job increasing your publication count, guys!):
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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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{256} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
http://www.fedoa.unina.it/593/
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{235} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-10995857[0] Corticostriatal activity in primary motor cortex of the macaque.
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{25} |
ref: Dum-2003.01
tags: cerebellum dentate_nucleus projections cerebrum prefrontal posterior_pareital M1 PM thalamus somatotopic
date: 03-11-2007 04:42 gmt
revision:2
[1] [0] [head]
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PMID-12522208 An unfolded map of the cerebellar dentate nucleus and its projections to the cerebral cortex
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{233} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-10380964 Monkey globus pallidus external segment neurons projecting to the neostriatum.
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{229} |
ref: notes-0
tags: SNR MSE error multidimensional mutual information
date: 03-08-2007 22:33 gmt
revision:2
[1] [0] [head]
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http://ieeexplore.ieee.org/iel5/516/3389/00116771.pdf or http://hardm.ath.cx:88/pdf/MultidimensionalSNR.pdf
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{225} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
First Experiments to test plasma confinement by magnetic dipole
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{223} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
calculations for a strong DC loop magnet using 1/8" copper capillary tubing:
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{176} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-15010499[0] Recursive Bayesian Decoding of Motor Cortical Signals by Particle Filtering
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{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 :) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{10} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
items 2-4 are not in accord with the stated purpose of leptin (1). | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{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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{76} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
experiment: animals initially learn that a light always lights up after pressing a switch which causes the administration of opioids. in successive trials the animal is not rewarded for pressing the switch, however the light (sometimes?) lights up following lever depression - and the animals continue to press the lever in the abscence of reward -- the reward has been transferred to the conditioned stimulus? http://druglibrary.org/schaffer/heroin/ase/chap_4.htm | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{141} |
ref: learning-0
tags: motor control primitives nonlinear feedback systems optimization
date: 0-0-2007 0:0
revision:0
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http://hardm.ath.cx:88/pdf/Schaal2003_LearningMotor.pdf not in pubmed. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{140} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-15649663 Composite adaptive control with locally weighted statistical learning.
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{145} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-12899266 Response error correction-a demonstration of improved human-machine performance using real-time EEG monitoring
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{147} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-12899253 Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis
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{148} |
ref: Vidal-2000.01
tags: EEG ERN error negativity conflict resolution 2000
date: 0-0-2007 0:0
revision:0
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PMID-10686362 Is the 'error negativity' specific to errors?
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{151} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PMID-11741014 Computational approaches to motor control. Tamar Flash and Terry Sejnowski.
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{167} |
ref: GarciaRill-1991.01
tags: PPN pedunculopontine nucleus brainstem sleep locomotion consciousness 1991
date: 0-0-2007 0:0
revision:0
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PMID-1887068 The Pedunculopontine nucleus
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{193} |
ref: Di-1994.06
tags: dopamine NMDA striatum globus_pallidus ion_channels neurotransmitters
date: 0-0-2007 0:0
revision:0
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PMID-7521083 Modulatory functions of neurotransmitters in the striatum: ACh/dopamine/NMDA interactions.
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{17} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
http://mattl.co.uk/apache2subversiondebianhowto.html pretty good. SSL and subversion seems to cause problems with apache2 on this 'unstable' Debian build - it complains of a BAD MAC header after a deterministic number of bytes are transmitted. Therefore I moved to port 80 from 443. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{28} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{31} |
ref: bookmark-0
tags: job_search professional employment wisdom
date: 0-0-2006 0:0
revision:0
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{34} |
ref: bookmark-0
tags: linear_algebra solution simultaneous_equations GPGPU GPU LUdecomposition clever
date: 0-0-2006 0:0
revision:0
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{44} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
http://www.jneurosci.org/cgi/reprint/24/12/2989.pdf
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{48} |
ref: bookmark-0
tags: Linux device_drivers memory virtual_memory PCI address_translation
date: 0-0-2006 0:0
revision:0
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http://www.tldp.org/LDP/khg/HyperNews/get/devices/addrxlate.html | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{57} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
http://www.cs.rug.nl/~rudy/matlab/
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{59} |
ref: bookmark-0
tags: statistics logistic regression binomial logit BIC AIC SPSS
date: 0-0-2006 0:0
revision:0
[head]
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http://www2.chass.ncsu.edu/garson/PA765/logistic.htm
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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
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http://prism.bham.ac.uk/pdf_files/SmithPred_93.PDF
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{66} |
ref: bookmark-0
tags: machine_learning classification entropy information
date: 0-0-2006 0:0
revision:0
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http://iridia.ulb.ac.be/~lazy/ -- Lazy Learning. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{70} |
ref: notes, bookmark-0
tags: spikes action_potentials neurons subthreshold depolarization c.elegans
date: 0-0-2006 0:0
revision:0
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"Millisecond-timescale, genetically targeted optical control of neural activity" http://www.nature.com/neuro/journal/v8/n9/full/nn1525.html what they did:
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{79} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
20% of the firing variance was accouted for by the gaze angle. the monkey was free to fixate where he wanted to in this experiment. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{81} |
ref: Stapleton-2006.04
tags: Stapleton Lavine poisson prediction gustatory discrimination statistical_model rats bayes BUGS
date: 0-0-2006 0:0
revision:0
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{84} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{86} | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
pretty simple, to add a .mp3 to all files in a directory (e.g. if they originally were on an old mac): rename 's/(.*)/$1.mp3/' *the expression in quotes is just a perl regular expression | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
{90} |
ref: bookmark-0
tags: mirror reflective projection lens design NEC optics
date: 0-0-2006 0:0
revision:0
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http://www.nec.co.jp/techrep/en/journal/g06/n03/060319.html very neat - and I'm surprised that they put all of this on the web! you can almost make one of these yourself with the information within. |