
Mechanisms, codes, noise and capacity in neural memory
Description
The ability of the brain to retain, work with, and recall information over time is believed to be underwritten by circuit processes that extend the short time-scales on which its units operate, and codes that mitigate the effects of noise and maximize capacity.
I will discuss how mechanistic but simplified models are helping to unravel the architectures underlying such circuits. As a particular example, I will focus on how the cortical grid cell response may emerge from neural interactions. A tight loop between theory, experiment, and quantitative data analysis, together with the fortuitous properties of the grid cell system, have allowed us to reach a level of understanding about mechanism which rivals that for orientation tuning in V1, one of the best-studied cortical systems in neuroscience.
I will next discuss how different neural codes can dramatically influence the capacity and fidelity of neural representations. I will describe a qualitatively new class of population codes that may enable N neurons to robustly represent an analog variable with a dynamic range that grows exponentially with N, in contrast to previously characterized neural codes, whose dynamic range grows only linearly with N. I will conclude by describing a recent theoretical construction for Hopfield networks, using expander graphs, that exhibits exponentially many, robust stable states, again in contrast to previous constructions that achieved only sub-exponential performance.
Speaker Bio
Ila Fiete is an Associate Professor in the Department of Neuroscience and the Institute for Neuroscience, at UT Austin. She obtained her Ph.D. at Harvard under the guidance of Sebastian Seung at MIT. Her postdoctoral work was at the Kavli Institute for Theoretical Physics at Santa Barbara, and at Caltech, where she was a Broad Fellow. Ila Fiete is a fellow in the Center for Learning and Memory, a McKnight Scholar, and an ONR Young Investigator. She has been an Alfred P. Sloan Foundation Fellow and a Searle Scholar.