Neural Implementation of Bayesian Inference in Population Codes

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Si Wu, Shun-ichi Amari


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 accu(cid:173) racy 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 con(cid:173) veyed by the change in recurrent interactions as a result of Hebbian learning.