Synaptic Theory of Replicator-Like Melioration

According to the theory of Melioration, organisms in repeated choice settings shift their choice preference in favor of the alternative that provides the highest return. The goal of this paper is to explain how this learning behavior can emerge from microscopic changes in the efficacies of synapses, in the context of a two-alternative repeated-choice experiment. I consider a large family of synaptic plasticity rules in which changes in synaptic efficacies are driven by the covariance between reward and neural activity. I construct a general framework that predicts the learning dynamics of any decision-making neural network that implements this synaptic plasticity rule and show that melioration naturally emerges in such networks. Moreover, the resultant learning dynamics follows the Replicator equation which is commonly used to phenomenologically describe changes in behavior in operant conditioning experiments. Several examples demonstrate how the learning rate of the network is affected by its properties and by the specifics of the plasticity rule. These results help bridge the gap between cellular physiology and learning behavior.