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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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{1235} | ||
PLoS One: PMID-23251670 Ultra-Bright and -Stable Red and Near-Infrared Squaraine Fluorophores for In Vivo Two-Photon Imaging
PMID-22056675 A gene-fusion strategy for stoichiometric and co-localized expression of light-gated membrane proteins
PMID-22056675 Substantial Generalization of Sensorimotor Learning from Bilateral to Unilateral Movement Conditions
PMID-23408972 Credit Assignment during Movement Reinforcement Learning
PMID-23382796 Visuomotor Learning Enhanced by Augmenting Instantaneous Trajectory Error Feedback during Reaching
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0054771 Flexible Switching of Feedback Control Mechanisms Allows for Learning of Different Task Dynamics
PMID-23365648 Recognizing Sights, Smells, and Sounds with Gnostic Fields
PMID-23300606 Decoding Hindlimb Movement for a Brain Machine Interface after a Complete Spinal Transection
Journal of Neural Engineering: PMID-23449002 Model-based rational feedback controller design for closed-loop deep brain stimulation of Parkinson's disease.
PMID-23428966 Improving brain-machine interface performance by decoding intended future movements.
PMID-23428937 An implantable wireless neural interface for recording cortical circuit dynamics in moving primates.
PMID-23428877 Local-learning-based neuron selection for grasping gesture prediction in motor brain machine interfaces.
PMID-22954906 Sparse decoding of multiple spike trains for brain-machine interfaces.
PMID-23010756 Comprehensive characterization and failure modes of tungsten microwire arrays in chronic neural implants.
PMID-23283391 Performance of conducting polymer electrodes for stimulating neuroprosthetics.
PMID-23160018 Properties and application of a multichannel integrated circuit for low-artifact, patterned electrical stimulation of neural tissue.
Nature Methods: PMID-23524393 Whole-brain functional imaging at cellular resolution using light-sheet microscopy
PMID-23142873 Two-photon optogenetics of dendritic spines and neural circuits
Nanowires, useful for Flip's idea.
Of personal interest: Richardson-Lucy (RL) deconvolution for sub-diffraction limit imaging. http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0056624 Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0055518 Brain Training Game Boosts Executive Functions, Working Memory and Processing
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0061390 Cognitive Training Improves Sleep Quality and Cognitive Function among Older Adults with Insomnia
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0054402 Perceived Multi-Tasking Ability, Impulsivity, and Sensation Seeking
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0052500 Learning and Long-Term Retention of Large-Scale Artificial Languages
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0052042 Non-Hebbian Learning Implementation in Light-Controlled Resistive Memory Devices
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0058284 Attractor Metabolic Networks
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0059196 Prenatal Exposure to a Polychlorinated Biphenyl (PCB) Congener Influences Fixation Duration on Biological Motion at 4-Months-Old: A Preliminary Study
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0060437 Hunger in the Absence of Caloric Restriction Improves Cognition and Attenuates Alzheimer's Disease Pathology in a Mouse Model
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{890} | ||
I'm an avid open-water swimmer, and other than the quarry and beach, I spend many fridays hoping the water in Falls lake is not too choppy. If it's glassy and smooth (and even sometimes when it's not), I can fall into the hypnotic 4/4 chug of stroke-stroke-stroke-breathe, stroke-str ... not hard, since the brown water is featureless, and the above-water scenery doesn't change much either. Several years ago I was out on Falls lake doing my thing, comfortably clear in the middle of the lake, heading back to the beach. In my unawareness I failed to notice that a thunderstorm had grown in the hot summer afternoon. Normally I'm rather debonaire about these things, but have been in places just before they were struck by lightning, and this felt a little like that. So, SOL Tim starts considering the rather limited options (god) (hold breath for as long as possible) (are they the same?). Just then, some Mexican guy on a kayak comes paddling out of ... nowhere ... and asks me if I need help. I bearhug the back of his boat and we get back to shore before the storm breaks. .... Another friday, another season and I set off with a friend clear across Falls lake, which is far, like 3mi round trip. I chat with a Mexican dude before we launch the ships; i guess he seems a bit familiar, but I'm too nervous, eager, and worrying about the thoughts/abilities of my friend to think much. That swim goes fine, minus all the damned speadboats and the ravenous hunger that sets in afterward. Yesterday I had intended to swim at a pool, but some toddling kid chose to contaminate it, and so back to Falls Lake. It's choppy and hard to swim, and I don't make it as far as intended; again before launching, I meet a Mexican dude, and he asks me if I'm crossing the lake again. I tell him no, not enough time; the water envelops, and I'm back in the swim coma, gone to the point when I get back the sun is down and the moon has risen. Surprisingly, when I get back the Mexican guy and his family are still there, slowly cleaning up BBQ debris by the light of highbeams and one crappy flashlight. It's cool and peaceful on the lake, but they probably should have left half an hour ago; as I go to the restroom to change, I wave to the guy and realize two things simultaneously: (1) fuck, it's been the same guy, (2) he may have delayed departure, gracefully and surreptitiously, until I was back. Curiosity makes me want to ask if he had, to see if coincidence licked me again, but that's not right; I did't. | ||
{865} | ||
This evening, on the drive back from wacky (and difficult) Russian-style yoga, I got a chance to explain to my brother what I really want to be working on, the thing that really tickles my fancy. My brother and I, so much as genetic commonality and common upbringing seem to effect, have very similar styles of thinking, which made explaining things a bit easier. For you, dear readier, I'll expand a bit. I'd like to write a program that writes other programs, iteratively, given some objective function / problem statement / environment in which to interact. The present concrete goal is to have a said program make a program that is able to lay out PCBs with quality similar to that of humans. The overarching framework that I'm planning on using is genetic/evolutionary algorithms (the latter does not have crossover, fyi), but no one has applied GA to the problem in this way: most people use GA to solve a particular instance of a problem. Rubbish, i say, this is energy wasteful! Rubbish, you may return: the stated problem requires a degree of generalization and disconnect from the 'real world' (the PCB) that makes GAs extremely unlikely to come up with any solutions. Expressed another way: the space to be explored is too large (program begets program begets solution). This is a very sensible critique; there is no way in hell a GA can solve this problem. They are notably pathetic at exploring space in a energy-efficient way (to conclude a paragraph again with energy... ). There are known solutions for this: memory -- cache the results, in terms of algorithm & behavior, of all 'hypotheses' or individuals tried out by a GA. This is what humans do -- they remember the results of their experiment, and substitute the result rather than running a test again. But humans do something far more sophisticated and interesting than just memory - they engineer systems; engineering is an iterative process that often goes down wrong design paths, yet it nonetheless delivers awesome things like Saabs and such. As I described to K--, engineering is not magic and can be (has been?) described mechanistically. First of all, most engineering artifacts start off from established, well-characterized components, aggregated through the panoply of history. Some of these components describe how other components are put together, things that are either learned in school or by taking things apart. Every engineer, ala Newton, stands on the vast shoulders of the designers before; hence any program must also have these shoulders available. The components are assembled into a system in a seemingly ad-hoc and iterative procedure: sometimes you don't know what you want, so you play with the parts sorta randomly, and see what interesting stuff comes out. Other times you know damn well what you / your boss / the evil warlord who holds you captive wants. Both modes are interesting (and the dichotomy is artificial), but the latter is more computer-like, hence to be modeled. Often the full details of the objective function or desired goal is very unclear in the hands of the boss / evil warlord (1), despite how reluctant they may be to admit this. Such an effect is well documented in Fred Brooks' book, __The Design of Design__. Likewise, how to get to a solution is unclear in the mind of an engineer, so he/she shuffles things around in the mind (2),
This search is applied iteratively, apparently a good bit of the time subconsciously. A component exists in our mind as a predictive model of how the thing behaves, so we simulate it on input, observe output, and check to see if anything there is correlated / decorrelated with target features. (One would imagine that our general purpose modeling ability grew from needing to model and predict the world and all the yummy food/dangerous animals/warlords in it). The bigger the number of internal models in the engineers mind, the bigger the engineers passion for the project, the more components can be simulated and selected for. Eventually progress is made, and a new subproblem is attacked in the same way, with a shorter path and different input/output to model/regress against. This is very non-magical, which may appall the more intuitive designers among us. It is also a real issue, because it doesn't (or poorly) explains really interesting engineering: e.g. the creation of the Fourier transform, the creation of the expectation-maximization algorithm, all the statistical and mathematical hardware that lends beauty and power to our design lives. When humans create these things, they are at the height of their creative ability, and thus it's probably a bit ridiculous to propose having a computer program do the same. That does not prevent me from poking at the mystery here, though: perhaps it is something akin to random component assembly (and these must be well known components (highly accurate, fast internal models); most all innovations were done by people exceptionally familiar with their territory), with verification against similarly intimately known data (hence, all things in memory - fast 'iteration cycles'). This is not dissimilar to evolutionary approaches to deriving laws. A Cornell physicist / computer scientist was able to generate natural laws via a calculus-infused GA {842}, and other programs were able to derive Copernicus' laws from planetary data. Most interesting scientific formulae are short, which makes them accessible to GAs (and also aesthetically pleasurable, and/or memelike, but hey!). In contrast engineering has many important design patterns that are borrowed by analogy from real-world phenomena, such as the watermark algorithm, sorting, simulated annealing, the MVC framework, object-oriented programming, WIMP interface, verb/noun interface, programming language, even GAs themselves! Douglas Hofstadter has much more to say about analogies, so I defer to him here. Irregardless, as K-- pointed out, without some model for creativity (even one as soulless as the one above), any proposed program-creating program will never come up with anything really new. To use a real-world analogy, at his work the boss is extremely crazy - namely, he mistook a circuit breaker for an elevator (in a one-story factory!). But, this boss also comes up with interminable and enthusiastic ideas, which he throws against the wall of his underlings a few dozen times a day. Usually these ideas are crap, but sometimes they are really good, and they stick. According to K--, the way his mind works is basically opaque and illogical (I've met a few of these myself), yet he performs an essential job in the company - he spontaneously creates new ideas. Without such a boss, he claimed, the creations of a program-creating-program will impoverished. And perhaps hence this should be the first step. Tonight I also learned that at the company (a large medical devices firm) they try to start projects at the most difficult step. That way, projects that are unlikely to succeed are killed as soon as possible. The alternate strategy, which I have previously followed, is to start with the easiest things first, so you get some motivation to continue. Hmm... The quandary to shuffle your internal models over tonight then, dear readers, is this: is creativity actually (or accurately modeled by) random component-combination creation (boss), followed by a selection/rejection (internal auditing, or colleague auditing)? (3)
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