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Optimal Neural Population Codes for High-dimensional Stimulus Variables
Zhuo Wang · Alan A Stocker · Daniel Lee

Thu Dec 05 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor

How does neural population process sensory information? Optimal coding theories assume that neural tuning curves are adapted to the prior distribution of the stimulus variable. Most of the previous work has discussed optimal solutions for only one-dimensional stimulus variables. Here, we expand some of these ideas and present new solutions that define optimal tuning curves for high-dimensional stimulus variables. We consider solutions for a minimal case where the number of neurons in the population is equal to the number of stimulus dimensions (diffeomorphic). In the case of two-dimensional stimulus variables, we analytically derive optimal solutions for different optimal criteria such as minimal L2 reconstruction error or maximal mutual information. For higher dimensional case, the learning rule to improve the population code is provided.

Author Information

Zhuo Wang (Facebook Reality Labs)
Alan A Stocker (University of Pennsylvania)
Daniel Lee (Samsung Research/Cornell University)

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