An Information Theoretic Approach to the Functional Classification of Neurons

Abstract

A population of neurons typically exhibits a broad diversity of responses to sensory inputs. The intuitive notion of functional classification is that cells can be clustered so that most of the diversity is captured by the iden- tity of the clusters rather than by individuals within clusters. We show how this intuition can be made precise using information theory, with- out any need to introduce a metric on the space of stimuli or responses. Applied to the retinal ganglion cells of the salamander, this approach re- covers classical results, but also provides clear evidence for subclasses beyond those identified previously. Further, we find that each of the gan- glion cells is functionally unique, and that even within the same subclass only a few spikes are needed to reliably distinguish between cells.