A neural implementation of Bayesian inference based on predictive coding

Predictive coding 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 predictive coding can perform almost exact Bayesian inference when applied to computing with population codes. It is demonstrated that the proposed algorithm, based on predictive coding, 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. Predictive coding 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.