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 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.

13 pages, 4 figures. To appear in Advances in Neural Information Processing Systems (NIPS) 15

References in corpus (2)

• The information bottleneck method
• Universality and individuality in a neural code

Cited by in corpus (4)

• Multivariate f-Divergence Estimation With Confidence
• Ensemble Estimation of Generalized Mutual Information with Applications to Genomics
• Coarse--graining and hints of scaling in a population of 1000+ neurons
• Non parametric estimation of joint, Renyi-Stallis entropies and mutual information and asymptotic limits