The Geometry of Information Coding in Correlated Neural Populations

TL;DR

This paper reviews the theoretical understanding of how noise correlations in neural populations affect information coding, emphasizing a geometric perspective and introducing a new approach to analyze the impact of correlation structures.

Contribution

It provides a comprehensive overview of existing theories on neural noise correlations and introduces a novel geometric framework for understanding their influence on neural coding.

Findings

Noise correlation structure critically influences neural coding fidelity.

Geometric analysis offers new insights into the impact of correlations.

A new approach enhances understanding of correlation effects on neural information encoding.

Abstract

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout we emphasize a geometrical picture of how noise correlations impact the neural code.