Combinatorial coding in neural populations

TL;DR

This paper demonstrates that a combinatorial neural code, tracking individual neuron activity and silences, conveys significantly more information about visual motion than average spike counts, challenging traditional cortical coding theories.

Contribution

It introduces and empirically validates a combinatorial coding scheme in cortical populations that enhances information transmission about sensory stimuli.

Findings

Combinatorial code doubles information compared to spike count.

Diverse firing dynamics contribute to code richness.

Code is robust to neuron-neuron correlations.

Abstract

To evaluate the nature of the neural code in the cerebral cortex, we have used a combination of theory and experiment to assess how information is represented in a realistic cortical population response. We have shown how a sensory stimulus could be estimated on a biologically-realistic time scale, given brief individual responses from a population of neurons with similar response properties. For neurons in extrastriate motion area MT, a combinatorial code, one that keeps track of the cell identity of action potentials and silences in individual neurons across the population, carries twice as much information about visual motion as does spike count averaged over the same group of cells. The combinatorial code is more informative because of the diverse firing rate dynamics of MT neurons in response to constant motion stimuli, and is robust to neuron-neuron correlations. We provide a theoretical motivation for these observations that challenges commonly held ideas about the nature of cortical coding at the level of single neurons and neural populations.