Reliability of Layered Neural Oscillator Networks

TL;DR

This paper investigates the reliability of large neural oscillator networks' responses to stimuli, analyzing individual neuron and overall network output consistency, and how different noise types influence this reliability.

Contribution

It provides a detailed analysis of neuronal and pooled response reliability in coupled neural oscillator networks, highlighting conditions affecting consistency and noise impacts.

Findings

Large networks tend to be reliably pooled despite individual neuron variability

Certain noise types significantly reduce response reliability

Network conditions determine individual neuron reliability

Abstract

We study the reliability of large networks of coupled neural oscillators in response to fluctuating stimuli. Reliability means that a stimulus elicits essentially identical responses upon repeated presentations. We view the problem on two scales: neuronal reliability, which concerns the repeatability of spike times of individual neurons embedded within a network, and pooled-response reliability, which addresses the repeatability of the total synaptic output from the network. We find that individual embedded neurons can be reliable or unreliable depending on network conditions, whereas pooled responses of sufficiently large networks are mostly reliable. We study also the effects of noise, and find that some types affect reliability more seriously than others.