Artificial Neural Networks and Machine Learning – ICANN 2012

Volume 7552 of the series Lecture Notes in Computer Science pp 523-530

Bayesian Inference with Efficient Neural Population Codes

  • Xue-Xin WeiAffiliated withDepartment of Psychology, University of Pennsylvania
  • , Alan A. StockerAffiliated withDepartment of Psychology, University of Pennsylvania

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The accuracy with which the brain can infer the value of a stimulus variable depends on both the amount of stimulus information that is represented in sensory neurons (encoding) and the mechanism by which this information is subsequently retrieved from the responses of these neurons (decoding). Previous studies have mainly focused on either the encoding or the decoding aspect. Here, we present a new framework that functionally links the two. More specifically, we demonstrate that optimal (efficient) population codes which guarantee uniform firing rate distributions allow the accurate emulation of optimal (Bayesian) inference using a biophysically plausible neural mechanism. The framework provides predictions for estimation bias and variability as a function of stimulus prior, strength and integration time, as well as physiological parameters such as tuning curves and spontaneous firing rates. Our framework represents an example of the duality between representation and computation in neural information processing.


Neural encoding Optimal decoding Population vector