m8ta
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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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{1301} | |||
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{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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{1085} | |||
PMID-21603228[0] Dopaminergic Balance between Reward Maximization and Policy Complexity.
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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 | |||
{932} | |||
PMID-18429703 Psychophysical evaluation for visual prosthesis.
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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). | |||
{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. | |||
{792} | |||
http://www.cs.cmu.edu/~wcohen/slipper/
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{713} | |||
PMID-11250009[0] Sleep and memory: a molecular perspective.
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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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{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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{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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{256} | |||
http://www.fedoa.unina.it/593/
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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. |