An information theoretic approach to the functional classification of neurons

TL;DR

This paper introduces an information theoretic method for classifying neurons based on their responses, revealing new subclasses and demonstrating the functional uniqueness of individual neurons without relying on stimulus-response metrics.

Contribution

It presents a novel information theoretic framework for neuron classification that uncovers previously unidentified subclasses and highlights the uniqueness of individual neurons.

Findings

Recovers classical neuron classification results

Identifies new subclasses of retinal ganglion cells

Shows that few spikes suffice to distinguish individual 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 in the identity of the clusters rather than by individuals within clusters. We show how this intuition can be made precise using information theory, without any need to introduce a metric on the space of stimuli or responses. Applied to the retinal ganglion cells of the salamander, this approach recovers classical results, but also provides clear evidence for subclasses beyond those identified previously. Further, we find that each of the ganglion cells is functionally unique, and that even within the same subclass only a few spikes are needed to reliably distinguish between cells.