The Geometry of Information Coding in Correlated Neural Populations

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 this review, we emphasize a geometrical picture of how noise correlations impact the neural code.

Automated summary

Neurons in the brain represent information through their collective activity, and the fidelity of neural population code depends on how variability in neuron responses is shared. Two decades of studies have shown the importance of noise correlations in neural coding, with theoretical developments and new approaches providing valuable insights. A geometrical picture of how noise correlations impact the neural code is emphasized in this review.