Spiking allows neurons to estimate their causal effect

Abstract

Neural plasticity can be seen as ultimately aiming at the maximization of reward. However, the world is complicated and nonlinear and so are neurons' firing properties. A neuron learning to make changes that lead to the maximization of reward is an estimation problem: would there be more reward if the neural activity had been different? Statistically, this is a causal inference problem. Here we show how the spiking discontinuity of neurons can be a tool to estimate the causal influence of a neuron's activity on reward. We show how it can be used to derive a novel learning rule that can operate in the presence of non-linearities and the confounding influence of other neurons. We establish a link between simple learning rules and an existing causal inference method from econometrics.