Network structure of cascading neural systems predicts stimulus propagation and recovery

TL;DR

This paper develops a theoretical framework linking neural network structure to stimulus propagation and recovery, validated with empirical data, highlighting the role of cycles in supporting cognitive functions like memory and attention.

Contribution

It introduces a novel theoretical approach combining multiple mathematical tools to explain how network structure influences neural dynamics and information processing.

Findings

Network structure can be designed to extend stimulus propagation and recovery.

Cycles are prevalent motifs that support sustained neural activity.

The theory is validated with empirical neural data.

Abstract

Many neural systems display cascading behavior characterized by uninterrupted sequences of neuronal firing. This gap precludes an understanding of how variations in network structure manifest in neural dynamics and either support or impinge upon information processing. Here, we develop a theoretical understanding of how network structure supports information processing through network dynamics, and we validate our theory with empirical data. Using a generalized spiking model and mathematical tools from linear systems theory, network control theory, and information theory, we show how network structure can be designed to temporally extend the propagation and recovery of certain stimulus patterns. Moreover, we observe cycles as structural and dynamic motifs that are prevalent in such networks. Broadly, our results demonstrate how cascading neural networks could contribute to cognitive faculties that require lasting activation of neuronal patterns, such as working memory or attention.