High-dimensional reservoir neural dynamics: rules and rewards

High-dimensional reservoir neural dynamics: rules and rewards

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Xiao-Jing Wang, Yale University
Fine Hall 214

Neural activity recorded in behaving animals is highly variable and heterogeneous, which is especially true for neurons in the prefrontal cortex (PFC), the so called 'CEO of the brain' of central importance to many cognitive functions. In this talk, I will present a reservoir-type model of randomly connected neurons to account for the diversity of neural signals in the prefrontal cortex. Specifically, I will show that such a network gives rise to mixed-selectivity of neurons that can encode task rules underlying flexible behaviors, and a broad range of time constants for short-term memory.