Learning neural codes for perceptual uncertainty

Perception is an inferential process, in which the state of the immediate environment must be estimated from sensory input. Inference in the face of noise and ambiguity requires reasoning with uncertainty, and much animal behaviour appears close to Bayes optimal. This observation has inspired hypotheses for how the activity of neurons in the brain might represent the distributional beliefs necessary to implement explicit Bayesian computation. While previous work has focused on the sufficiency of these hypothesised codes for computation, relatively little consideration has been given to optimality in the representation itself. Here, we adopt an encoder-decoder approach to study representational optimisation within one hypothesised belief encoding framework: the distributed distributional code (DDC). We consider a setting in which typical belief distribution functions take the form of a sparse combination of an underlying set of basis functions, and the corresponding DDC signals are corrupted by neural variability. We estimate the conditional entropy over beliefs induced by these DDC signals using an appropriate decoder. Like other hypothesised frameworks, a DDC representation of a belief depends on a set of fixed encoding functions that are usually set arbitrarily. Our approach allows us to seek the encoding functions that minimise the decoder conditional entropy and thus optimise representational accuracy in an information theoretic sense. We apply the approach to show how optimal encoding properties may adapt to represent beliefs in new environments, relating the results to experimentally reported neural responses.