Iterative Population Decoding Based on Prior Beliefs

Abstract

We propose a framework for investigation of the modulation of neural coding/decoding by the availability of prior information on the stimulus statistics. In particular, we describe a novel iterative decoding scheme for a population code that is based on prior information. It can be viewed as a generalization of the Richardson-Lucy algorithm to include degrees of belief that the encoding population encodes specific features. The method is applied to a signal detection task and it is verified that - in comparison to standard maximum-likelihood decoding - the procedure significantly enhances performance of an ideal observer if appropriate prior information is available. Moreover, the model predicts that high prior probabilities should lead to a selective sharpening of the tuning profiles of the corresponding recurrent weights similar to the shrinking of receptive fields under attentional demands that has been observed experimentally.