"A Bayesian theory of efficient neural coding"

Barlow's "efficient coding hypothesis" considers neural codes to be efficient if they maximize information transfer between stimuli and neural responses.

Bag Lunch Talk

Abstract: Barlow's "efficient coding hypothesis" considers neural codes to be efficient if they maximize information transfer between stimuli and neural responses. This idea has provided a guiding theoretical framework for the study of coding in neural systems and inspired a large number of experimental and theoretical studies. More recently, a theory of probabilistic neural coding known as the "Bayesian brain hypothesis" has attracted considerable attention in systems neuroscience. However, there does not appear as yet to be any clear connection between these two paradigms. In this talk, I will introduce a Bayesian theory of efficient coding, which has Barlow's efficient coding hypothesis as a special case. I will argue that there is nothing privileged about information-maximizing codes; they are ideal for some tasks but sub-optimal for many others. Bayesian efficient coding substantially enlarges the family of normatively optimal codes and provides a general framework for understanding the principles of sensory encoding. I will derive Bayesian efficient codes for a few simple examples, show an application to neural data, and suggest several avenues for future research.