Simultaneous silence organizes structured higher-order interactions in neural populations.

Hideaki Shimazaki,Tomoe Ishikawa,Kolia Sadeghi,Taro Toyoizumi,Yuji Ikegaya

doi:10.1038/srep09821

Abstract

Activity patterns of neural population are constrained by underlying biological mechanisms. These patterns are characterized not only by individual activity rates and pairwise correlations but also by statistical dependencies among groups of neurons larger than two, known as higher-order interactions (HOIs). While HOIs are ubiquitous in neural activity, primary characteristics of HOIs remain unknown. Here, we report that simultaneous silence (SS) of neurons concisely summarizes neural HOIs. Spontaneously active neurons in cultured hippocampal slices express SS that is more frequent than predicted by their individual activity rates and pairwise correlations. The SS explains structured HOIs seen in the data, namely, alternating signs at successive interaction orders. Inhibitory neurons are necessary to maintain significant SS. The structured HOIs predicted by SS were observed in a simple neural population model characterized by spiking nonlinearity and correlated input. These results suggest that SS is a ubiquitous feature of HOIs that constrain neural activity patterns and can influence information processing.

Highlights

Activity patterns of neural population are constrained by underlying biological mechanisms
These results suggest that simultaneous silence (SS) is a ubiquitous feature of higher-order interactions (HOIs) that constrain neural activity patterns and can influence information processing
We investigated the structure of HOIs in spontaneous activity of neurons in the CA3 area of organotypic hippocampal slice cultures

Summary

Introduction

Activity patterns of neural population are constrained by underlying biological mechanisms These patterns are characterized by individual activity rates and pairwise correlations and by statistical dependencies among groups of neurons larger than two, known as higher-order interactions (HOIs). The structured HOIs predicted by SS were observed in a simple neural population model characterized by spiking nonlinearity and correlated input These results suggest that SS is a ubiquitous feature of HOIs that constrain neural activity patterns and can influence information processing. Individual activity rates and pairwise correlations alone could explain , 90% of variability in activity patterns of small populations of retinal ganglion cells[8,9] and cortical neurons[12,13] This does not exclude the existence of HOIs or limit their contribution to information processing. The significance of SS in characterizing HOIs of the population activity is not well understood

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Conclusion