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{1577} | |||||||
Sketch - Program synthesis by sketching
The essential algorithm, in words: Take the sketch, expand it to a set of parameterized variables, holes, and calling contexts. Convert these to a DAG aka (?) data-code flow graph w/ dependencies. Try to simplify the DAG, one-hot encode integers, and convert to either a conjunctive-normal-form (CNF) SAT problem for MiniSat, or to a boolean circuit for the ABC solver. Apply MiniSat or ABC to the problem to select a set of control values = values for the holes & permutations that satisfy the boolean constraints. Using this solution, use the SAT solver to find a input variable configuration that does not satisfy the problem. This serves as a counter-example. Run this through the validator function (oracle) to see what it does; use the counter-example and (inputs and outputs) to add clauses to the SAT problem. Run several times until either no counter-examples can be found or the problem is `unsat`. Though the thesis describes a system that was academic & relatively small back in 2008, Sketch has enjoyed continuous development, and remains used. I find the work that went into it to be remarkable and impressive -- even with incremental improvements, you need accurate expansion of the language & manipulations to show proof-of-principle. Left wondering what limits its application to even larger problems -- need for a higher-level loop that further subdivides / factorizes the problem, or DFS for filling out elements of the sketch? Interesting links discovered in while reading the dissertation:
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{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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{1396} | |||||||
PMID-27791052 Ultrathin, transferred layers of thermally grown silicon dioxide as biofluid barriers for biointegrated flexible electronic systems
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{1250} |
ref: -0
tags: polyimide electrodes thermosonic bonding Stieglitz adhesion delamination
date: 03-06-2017 21:58 gmt
revision:7
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IEEE-6347149 (pdf) Improved polyimide thin-film electrodes for neural implants 2012
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{1380} |
ref: -0
tags: myoelectric EMG recording TMR prosthetics
date: 02-13-2017 20:43 gmt
revision:0
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PMID: Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation
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{1335} | |||||||
What are the concentrations of the monoamines in the brain? (Purpose: estimate the required electrochemical sensing area & efficiency)
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{1204} | |||||||
PMID-21867803[0] Can histology solve the riddle of the nonfunctioning electrode? Factors influencing the biocompatibility of brain machine interfaces.
____References____
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{1325} | |||||||
https://mitpress.mit.edu/sites/default/files/titles/free_download/9780262526548_Art_of_Insight.pdf | |||||||
{1301} | |||||||
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{1256} | |||||||
PMID-23393413 Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease.
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{1211} | |||||||
PMID-9723616[0] Signal-dependent noise determines motor planning.
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{1026} | |||||||
PMID-21298109[0] Implant size and fixation mode strongly influence tissue reactions in the CNS.
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{749} | |||||||
PMID-17266019[0] The brain tissue response to implanted silicon microelectrode arrays is increased when the device is tethered to the skull. ____References____
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{1168} |
ref: -0
tags: debian linux github persistent ssh authentication
date: 07-27-2012 01:40 gmt
revision:1
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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! | |||||||
{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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{1133} | |||||||
PMID-21875864 Dopamine cell transplantation in Parkinson's disease: challenge and perspective.
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{1129} | |||||||
PMID-15272269 Stem cell therapy for human neurodegenerative disorders-how to make it work.
Stroke:
ALS:
Synthesis:
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{1125} |
ref: -0
tags: active filter design Netherlands Gerrit Groenewold
date: 02-17-2012 20:27 gmt
revision:0
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IEEE-04268406 (pdf) Noise and Group Delay in Actvie Filters
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{1121} | |||||||
PMID-20970382 Gene delivery of AAV2-neurturin for Parkinson's disease: a double-blind, randomised, controlled trial.
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{1110} |
ref: -0
tags: Seymour thesis electrode lithography fabrication
date: 02-05-2012 17:35 gmt
revision:4
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Advanced polymer-based microfabricated neural probes using biologically driven designs.
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{1085} | |||||||
PMID-21603228[0] Dopaminergic Balance between Reward Maximization and Policy Complexity.
____References____
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{911} | |||||||
PMID-19621062 Emergence of a stable cortical map for neuroprosthetic control.
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{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: | |||||||
{262} | |||||||
clementine, 040207, Miguel's sorting. top 200 lags selected via bmisql.m , decent SNR on all channels but I had to z-score the state and measurement matricies. -- standard wiener -- linear kalman. -- associated behavior | |||||||
{219} | |||||||
PMID-6794389[0] Single neuron recording from motor cortex as a possible source of signals for control of external devices
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{496} | |||||||
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{5} |
ref: bookmark-0
tags: machine_learning research_blog parallel_computing bayes active_learning information_theory reinforcement_learning
date: 12-31-2011 19:30 gmt
revision:3
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hunch.net interesting posts:
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{932} | |||||||
PMID-18429703 Psychophysical evaluation for visual prosthesis.
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{922} | |||||||
PMID-20011034[0] A Wireless Brain-Machine Interface for Real-Time Speech Synthesis
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{323} |
ref: Loewenstein-2006.1
tags: reinforcement learning operant conditioning neural networks theory
date: 12-07-2011 03:36 gmt
revision:4
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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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{702} | |||||||
PMID-8670641[0] The hippocampo-neocortical dialogue.
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{718} | |||||||
Timetable / Plan:
Contingency Plan:
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{892} | |||||||
Open letter proposing some ideas on how to automate programming: simulate a human! Rather from a neuro background, and rather sketchy (as in vague, not as in the present slang usage). | |||||||
{864} | |||||||
Interesting ideas from __This Will Change Everything__
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{837} | |||||||
Hutter's Theorem: for all problems asymptotically large enough, there exists one algorithm that is within a factor of 5 as fast as the fastest algorithm for a particular problem. http://www.hutter1.net/ai/pfastprg.htm | |||||||
{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 ;-) | |||||||
{809} | |||||||
I learned this in college, but have forgotten all the details - Microcontroller provides an alternative to DDS where is the sampling frequency. F ranges from -0.2 to 0. | |||||||
{794} | |||||||
http://weblog.raganwald.com/2007/06/which-theory-first-evidence.html
also from that site - http://weblog.raganwald.com/2007/05/not-so-big-software-application.html
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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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{734} | |||||||
Rethinking the American Dream by David Kamp
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{713} | |||||||
PMID-11250009[0] Sleep and memory: a molecular perspective.
____References____
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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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{579} | |||||||
oldies but goodies:
Both are in Debian of course :) | |||||||
{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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{465} | |||||||
good explanation of 32-bit CRC (from the blackfin BF537 hardware ref): | |||||||
{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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{354} | |||||||
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{278} | |||||||
PMID-17143147[0] Decoding movement intent from human premotor cortex neurons for neural prosthetic applications
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{292} | |||||||
PMID-15217341[0] Cortical Neuro Prosthetics
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{256} | |||||||
http://www.fedoa.unina.it/593/
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{243} | |||||||
PMID-17035544 Dopaminergic control of sleep-wake states.
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properties of the brain: 1 .cerebellum in a supervised learner, I agree with the evidence: it learns to predict future outcomes given present states very efficiently. Appears to have a structure that is conducive for learning spatio-temporal structures, with the parallel fibers and purkinje cells. climbing fibers fire on error and cause LTP. Purkinje cells have inhibitor output -> hence error to LTP to less inhibition = movement in the positive direction. Mossy fibers have collaterals to DCN neurons and purkinje cells, i think. this whole structure seems rather strange to me - why the multiple levels of inversion? it is the same as the basal ganglia - striatal output is inhibitory upon the globus pallidus, globus pallidus output is inhibitory on the thalamus. {and, at least in the monkey though probably also in the human, the thalamus is very large and very well organized}. actually, the whole brain seems exceedingly well organized, the problem is that we don't really understand this organization quite yet. E.G the putamen seems to have a somatotopic organization & has units which fire according to motion in the distal joints. (those old papers are great!) . caudate seems to have some sort of cognitive role? blaaa. so, what does the brain do? it learns to live, more or less; it is adaptive. humans seem to be thte most adaptive; we stay in the adaptive phase for the longest part of our life, whereas rhesus seem to grow up rather quickly. learning! as kawato's student explains, learning modifying a function to minimize (or maximize) some evaluative function. In the case the fitness function is some function of the match between desired output and training output, that learning is supervised. We have neural networks to do this, and undoubtably the human mind can do this too. In the case the fitness function is some weighted-sum of a scalar reward, then you have reinforcement learning. Generally, the animal will learn the value of certain states, actions, or state-action pairs, and has to choose which is the best based on either the direct perceived value or the integrated expected future value. Humans think in this way all the time, and use a high-level model of the world, learned basically by example, trial and error, and even book-learning, to 'do the integral' and evaluate which of several paths are best. Once we 'decide', things then become habits. We, and especially monkeys, are exceptionally subject to choosing arbitrarily when the reward is unknown - we explore all of our lives, in order to expand the quality of our models of the world, and improve the reward-evaluation of states and actions. Is this dichotomy between models and evaluations artificial? Is there any reason to believe that they are represented in separate structures/pathways/molecules in the brain? perhaps. take dopamine for example. blocking its reuptake via cocaine is very rewarding, and induces a habit in mammals that are administered the drug. but perhaps it it not so much involved in reward so much as desire. {drug addicts who have their DA1 receptor blocked end up taking /more/ drugs, apparently in the desire to feel something}. DA depletion in parkinsons makes the stick larger in carrot-stick learning: these patients learn worse with reward than controls. {hence, error must not require DA}. _{system function is hard to intuit from such nonspecific effectors like drugs because the system is adaptive; i actually think leasions are better, or at least seem better, due to the precise organization fo the brain. anyway, learning. "the controller learns the inverse model of it's own reflexes" - this is brilliant. only through hebbian learning! I like this a lot. In general, i agree with Kawato (actually, so far everything he has put out seems to be high-quality, well thought out and easy to understand) - the proof is incontrovertible that there are inverse models in the brain, probably at least in the cerebellum. todo: review what is required to make an inverse model. ok time to put the monkey away. | |||||||
{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 :) | |||||||
{72} |
ref: abstract-0
tags: tlh24 error signals in the cortex and basal ganglia reinforcement_learning gradient_descent motor_learning
date: 0-0-2006 0:0
revision:0
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Title: Error signals in the cortex and basal ganglia. Abstract: Numerous studies have found correlations between measures of neural activity, from single unit recordings to aggregate measures such as EEG, to motor behavior. Two general themes have emerged from this research: neurons are generally broadly tuned and are often arrayed in spatial maps. It is hypothesized that these are two features of a larger hierarchal structure of spatial and temporal transforms that allow mappings to procure complex behaviors from abstract goals, or similarly, complex sensory information to produce simple percepts. Much theoretical work has proved the suitability of this organization to both generate behavior and extract relevant information from the world. It is generally agreed that most transforms enacted by the cortex and basal ganglia are learned rather than genetically encoded. Therefore, it is the characterization of the learning process that describes the computational nature of the brain; the descriptions of the basis functions themselves are more descriptive of the brain’s environment. Here we hypothesize that learning in the mammalian brain is a stochastic maximization of reward and transform predictability, and a minimization of transform complexity and latency. It is probable that the optimizations employed in learning include both components of gradient descent and competitive elimination, which are two large classes of algorithms explored extensively in the field of machine learning. The former method requires the existence of a vectoral error signal, while the latter is less restrictive, and requires at least a scalar evaluator. We will look for the existence of candidate error or evaluator signals in the cortex and basal ganglia during force-field learning where the motor error is task-relevant and explicitly provided to the subject. By simultaneously recording large populations of neurons from multiple brain areas we can probe the existence of error or evaluator signals by measuring the stochastic relationship and predictive ability of neural activity to the provided error signal. From this data we will also be able to track dependence of neural tuning trajectory on trial-by-trial success; if the cortex operates under minimization principles, then tuning change will have a temporal relationship to reward. The overarching goal of this research is to look for one aspect of motor learning – the error signal – with the hope of using this data to better understand the normal function of the cortex and basal ganglia, and how this normal function is related to the symptoms caused by disease and lesions of the brain. | |||||||
{26} | |||||||
http://www.thomaslockehobbs.com/ -- interetsing photoblog of a globetrotter & laconic harvard intellectual http://www.uni-weimar.de/architektur/InfAR/lehre/Entwurf/Patterns/107/ca_107.html Modern buildings are often shaped with no concern for natural light - they depend almost antirely on artificial light. But buildings which displace natural light as the major source of illumination are not fit places to spend the day. |