The Stanford Institute for Neuro-Innovation & Translational Neurosciences
proudly announces a research seminar by

Ila Fiete

"A new performance class of population codes in the brain"

Ila Fiete PhD
Assistant Professor of Neurobiology
Center for Learning and Memory
University of Texas at Austin

Website: Fiete lab Web Site

A conversation with Ila Fiete with the Stanford group Neuwrite West can be streamed or downloaded here: Fiete conversation

Abstract:

Neural representation is inherently noisy. Representational accuracy may be increased by encoding variables in the responses of a population of neurons. Most known population codes for continuous variables can at best reduce squared error by a factor of N, where N is the number of neurons involved in the representation.

Is it possible to do better?

I will discuss an analysis of the peculiar grid cell code for spatial location in mammals, showing that the brain can represent variables to allow exponential, rather than polynomial, increases in information content, with N.

I will present data and a model of human short-term memory, as both item number and delay time are varied, and argue that human short-term memory performance is consistent with the possibility that the brain actually exploits exponentially strong population codes.

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