Thinking About The Brain

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 examplesfrom 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.

The Role of Input Noise in Transcriptional Regulation

“…Although there are caveats, these conclusions are consistent with the idea that, as with other critical biological processes [18], [22], [42], [43], the regulation of gene expression can operate with a precision limited by fundamental physical principles.…”

Cognitive Engineering of Automated Assembly Processes

“…This shift is due to rapid progress in automatic control technology, which means that predominantly rule-based and skill-based components of human performance are becoming more and more automated (Usher & Kaber, 2000). However, there are convincing theoretical arguments (Bialek, 2002;Dreyfus, 1992;Harel, 2000) that the "ultimate cognitive machine," as a serious alternative to human ingenuity, to creativity, and to proactivity, is not on the horizon-and may never be. Therefore, the decisions and actions of the human when planning, monitoring, and optimizing complex production processes and the associated product structures will also be crucial in the future.…”

Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions

“…Observing the full dynamic range of neural responses may require using stimulus ensembles which approximate those occurring in nature (Rieke et al, 1997;Simoncelli & Olshausen, 2001), and it is an attractive hypothesis that the neural representation of these natural signals may be optimized in some way (Barlow, 1961(Barlow, , 2001von der Twer & Macleod, 2001;Bialek, 2002). Many neurons exhibit strongly nonlinear and adaptive responses that are unlikely to be predicted from a combination of responses to simple stimuli; for example neurons have been shown to adapt to the distribution of sensory inputs, so that any characterization of these responses will depend on context (Smirnakis et al, 1996;Brenner et al, 2000a;Fairhall et al, 2001).…”