Chaos and reliability in balanced spiking networks with temporal drive

TL;DR

This paper investigates how large, complex neural networks can produce reliable spike responses despite chaotic dynamics, revealing that intermittent reliability can coexist with chaos and has implications for neural coding.

Contribution

It demonstrates that chaos in balanced spiking networks does not preclude reliable spike timing, elucidating the dynamical mechanisms behind intermittent reliability.

Findings

Chaos persists with stimuli but does not prevent reliability

Intermittent periods of reliable spiking coexist with chaos

Reliability relates to specific time-dependent attractors

Abstract

Biological information processing is often carried out by complex networks of interconnected dynamical units. A basic question about such networks is that of reliability: if the same signal is presented many times with the network in different initial states, will the system entrain to the signal in a repeatable way? Reliability is of particular interest in neuroscience, where large, complex networks of excitatory and inhibitory cells are ubiquitous. These networks are known to autonomously produce strongly chaotic dynamics - an obvious threat to reliability. Here, we show that such chaos persists in the presence of weak and strong stimuli, but that even in the presence of chaos, intermittent periods of highly reliable spiking often coexist with unreliable activity. We elucidate the local dynamical mechanisms involved in this intermittent reliability, and investigate the relationship between this phenomenon and certain time-dependent attractors arising from the dynamics. A conclusion is that chaotic dynamics do not have to be an obstacle to precise spike responses, a fact with implications for signal coding in large networks.