Bayesian Inference with Efficient Neural Population Codes

Xue-Xin Wei and Alan A Stocker
ICANN International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 2012.

Published in: Springer Lecture Notes in Computer Science, 2012, Volume 7552, pages 523-530.
DOI: 10.1007/978-3-642-33269-2_66.

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) infer- ence 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 physiologi- cal 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.

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