The structures and functions of correlations in neural population codes

In this Review, Panzeri, Moroni, Safaai and Harvey explain how the levels and structures of correlations among the activity of neurons in a population shape information encoding, transmission and readout, and describe how future research could determine how the structures of correlations are optimized. The collective activity of a population of neurons, beyond the properties of individual cells, is crucial for many brain functions. A fundamental question is how activity correlations between neurons affect how neural populations process information. Over the past 30 years, major progress has been made on how the levels and structures of correlations shape the encoding of information in population codes. Correlations influence population coding through the organization of pairwise-activity correlations with respect to the similarity of tuning of individual neurons, by their stimulus modulation and by the presence of higher-order correlations. Recent work has shown that correlations also profoundly shape other important functions performed by neural populations, including generating codes across multiple timescales and facilitating information transmission to, and readout by, downstream brain areas to guide behaviour. Here, we review this recent work and discuss how the structures of correlations can have opposite effects on the different functions of neural populations, thus creating trade-offs and constraints for the structure-function relationships of population codes. Further, we present ideas on how to combine large-scale simultaneous recordings of neural populations, computational models, analyses of behaviour, optogenetics and anatomy to unravel how the structures of correlations might be optimized to serve multiple functions.

Automated summary

In this Review, Panzeri, Moroni, Safaai and Harvey discuss the effects of correlations among the activity of neurons in a population on information encoding, transmission, and readout. They also suggest future research directions on determining the optimization of correlation structures.