Thinking about the brain Export

@article{citeulike:89978, abstract = {We all are fascinated by the phenomena of intelligent behavior, as generated both by our own brains and by the brains of other animals. As physicists we would like to understand if there are some general principles that govern the structure and dynamics of the neural circuits that underlie these phenomena. At the molecular level there is an extraordinary universality, but these mechanisms are surprisingly complex. This raises the question of how the brain selects from these diverse mechanisms and adapts to compute "the right thing" in each context. One approach is to ask what problems the brain really solves. There are several examples - from the ability of the visual system to count photons on a dark night to our gestalt recognition of statistical tendencies toward symmetry in random patterns - where the performance of the system in fact approaches some fundamental physical or statistical limits. This suggests that some sort of optimization principles may be at work, and there are examples where these principles have been formulated clearly and generated predictions which are confirmed in new experiments; a central theme in this work is the matching of the coding and computational strategies of the brain to the statistical structure of the world around us. Extension of these principles to the problem of learning leads us into interesting theoretical questions about how to measure the complexity of the data from which we learn and the complexity of the models that we use in learning, as well as opening some new opportunities for experiment. This combination of theoretical and experimental work gives us some new (if still speculative) perspectives on classical problems and controversies in cognition.}, archivePrefix = {arXiv}, author = {Bialek, William}, citeulike-article-id = {89978}, citeulike-linkout-0 = {http://arxiv.org/abs/physics/0205030}, citeulike-linkout-1 = {http://arxiv.org/pdf/physics/0205030}, day = {9}, eprint = {physics/0205030}, keywords = {analysis, complex, func, information-theory, neural, statistics}, month = {Jul}, posted-at = {2009-10-06 08:22:11}, priority = {2}, title = {Thinking about the brain}, url = {http://arxiv.org/abs/physics/0205030}, year = {2002} }

TY - JOUR ID - citeulike:89978 L3 - citeulike-article-id:89978 N2 - We all are fascinated by the phenomena of intelligent behavior, as generated both by our own brains and by the brains of other animals. As physicists we would like to understand if there are some general principles that govern the structure and dynamics of the neural circuits that underlie these phenomena. At the molecular level there is an extraordinary universality, but these mechanisms are surprisingly complex. This raises the question of how the brain selects from these diverse mechanisms and adapts to compute "the right thing" in each context. One approach is to ask what problems the brain really solves. There are several examples - from the ability of the visual system to count photons on a dark night to our gestalt recognition of statistical tendencies toward symmetry in random patterns - where the performance of the system in fact approaches some fundamental physical or statistical limits. This suggests that some sort of optimization principles may be at work, and there are examples where these principles have been formulated clearly and generated predictions which are confirmed in new experiments; a central theme in this work is the matching of the coding and computational strategies of the brain to the statistical structure of the world around us. Extension of these principles to the problem of learning leads us into interesting theoretical questions about how to measure the complexity of the data from which we learn and the complexity of the models that we use in learning, as well as opening some new opportunities for experiment. This combination of theoretical and experimental work gives us some new (if still speculative) perspectives on classical problems and controversies in cognition. TI - Thinking about the brain KW - analysis KW - complex KW - func KW - information-theory KW - neural KW - statistics AU - Bialek, William PY - 2002/Jul/09/ UR - http://arxiv.org/abs/physics/0205030 ER -