Reward prediction error neurons implement an efficient code for reward

Abstract

We apply efficient coding principles to derive the optimal population of neurons to encode rewards from a distribution. Similar to this optimal population, dopaminergic reward prediction error neurons have a broad distribution of optimistically placed thresholds, neurons with higher thresholds have higher gain and the curvature of their responses depends on the threshold. Thus, these neurons may broadcast an efficient reward signal, not necessarily a reward prediction error.