Variational Information Maximization for Population Coding

The goal of neural processing assemblies is varied, and in many cases still rather unclear. However, a possibly reasonable subgoal is that sensory information may be encoded efficiently in a population of neurons. In this context, Mutual Information is a long studied measure of coding efficiency, and many attempts to apply this to {\em population coding} have been made. However, this is a numerically intractable task, and most previous studies redefine the criterion in forms of an approximation to Mutual Information, the Fisher Information being one such well-known approach. Here we describe a principled bound maximisation procedure for Mutual Information learning of population codes in a simple point neural model, and compare it with other approaches.