Demonstration that a theory of cortical function (“predictive coding”) can perform bayesian inference in some tasks, with a nice related work of physiological foundations of probability distribution representation in neurons and of bayesian inference

M. W. Spratling, A neural implementation of Bayesian inference based on predictive coding, Connection Science, Volume 28, 2016 – Issue 4, DOI: 10.1080/09540091.2016.1243655.

Predictive coding (PC) is a leading theory of cortical function that has previously been shown to explain a great deal of neurophysiological and psychophysical data. Here it is shown that PC can perform almost exact Bayesian inference when applied to computing with population codes. It is demonstrated that the proposed algorithm, based on PC, can: decode probability distributions encoded as noisy population codes; combine priors with likelihoods to calculate posteriors; perform cue integration and cue segregation; perform function approximation; be extended to perform hierarchical inference; simultaneously represent and reason about multiple stimuli; and perform inference with multi-modal and non-Gaussian probability distributions. PC thus provides a neural network-based method for performing probabilistic computation and provides a simple, yet comprehensive, theory of how the cerebral cortex performs Bayesian inference.

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