The seminar is sponsored by NYU-ECNU Center for Computational Chemistry at NYU Shanghai
Abstract:
In the brain, massive interactions between neurons through synapses give rise to rich dynamics and have been thought to be critical for brain computation. In this talk, I will discuss models of cortical circuits for learning and memory. In the first part, I will discuss network models for short-term memory based on the principle of corrective feedback (Lim and Goldman, 2013, Lim and Goldman, 2014), which is shown to be more robust against commonly considered perturbations in neural circuits than previously suggested models. In the second part, I will discuss synaptic plasticity underlying learning and long-term memory, and show a method to infer synaptic plasticity rules from firing rates of cortical neurons (Lim et al. 2015).
Biography:
Sukbin Lim obtained her Ph. D. in Mathematics from New York University in 2009. From 2009-2015, she was a postdoctoral scholar in the Center for Neuroscience at University of California, Davis, and in the Department of Neurobiology at The University of Chicago. She is currently an assistant professor of neural and cognitive sciences at NYU Shanghai. Her research focuses on modeling and analysis of neuronal systems. Utilizing a broad spectrum of dynamical systems theory, the theory of stochastic processes, and information and control theories, she develop and analyze neural network models and synaptic plasticity rules for learning and memory.