Neural Implementation of Bayesian Inference in Population Codes

This study investigates a population decoding paradigm, in which the estimation of stimulus in the previous step is used as prior knowledge for consecutive decoding. We analyze the decoding accuracy of such a Bayesian decoder (Maximum a Posteriori Estimate), and show that it can be implemented by a biologically plausible recurrent network, where the prior knowledge of stimulus is conveyed by the change in recurrent interactions as a result of Hebbian learning.

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