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Noise in the Brain: Statistical and Dynamical Perspectives

Banff International Research Station for Mathematical Innovation and Discovery

There is growing interest in applying statistical estimation methods to dynamical systems arising in neuroscience. The discipline of statistics provides an intellectual framework for quantifying and managing uncertainty. For statistical methods to apply, one must consider a system with some variability. Depending on where one locates the source of variability, different methods suggest themselves. The talk will discuss some challenges and opportunities in linking statistical and mathematical perspectives in theoretical neuroscience, including the problem of quantifying "phase resetting" in stochastic oscillators, the problem of identifying the most significant sources of noise in a finite state Markov model, the problem of inferring control mechanisms in brain-body motor control systems, and the problem of parameter identification in noisy conductance-based models.

Mathematics; Statistics; Probability theory and stochastic processes; Applied statistics

video/mp4

Author affiliation: Case Western Reserve University

2017-08-27

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/62832

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/