Learning temporal patterns: computational and experimental studies of timing

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DESCRIPTION (provided by applicant): The human brain remains the most sophisticated computational system known to man. Elucidating the mechanisms underlying the cerebral cortex's ability to generate behavior and cognition is critical for understanding both normal cortical processing and a myriad of neurological disorders produced by abnormal cortical function. A necessary step towards this goal will be to understand how the brain tells time and processes temporal information. Here we focus on the problem of generating and learning complex spatiotemporal patterns. The studies proposed here are based on the hypothesis that the internal dynamics of recurrent neural networks underlies some forms of timing in the range of hundreds of milliseconds to a few seconds, and on the recently proposed paradigm that time is encoded in the continuously changing activity pattern of a neuronal population. The proposal consists of two aims. In the first we will use a novel human psychophysical task to study the learning of temporal patterns and test explicit theoretical predictions of our hypothesis. In the second aim we will develop a computational model of timing as an implementation of the proposed paradigm, to determine whether it can account for the experimental results. PUBLIC HEALTH RELEVANCE: The ability to tell time and process temporal information is of fundamental importance to sensory and motor processing, behavior, learning, and cognition. And it is increasingly clear that the cognitive abnormalities in a number of neurological diseases-including learning disabilities, Parkinson's disease, and schizophrenia- are associated with deficits in the ability to normally process temporal information. Thus elucidating both normal and pathological brain function will require that we unveil the mechanisms that allow the brain to tell time. The current project focuses on this problem, not only by directly studying temporal processing, but by taking the important step towards understanding how complex computations emerge from the dynamics of recurrent neural circuits.