Selective Inhibition and Recruitment of Linear-Threshold Thalamocortical Networks

TL;DR

This paper models thalamocortical networks using linear-threshold systems to analyze how the thalamus enables selective inhibition and recruitment, enhancing network robustness and control efficiency.

Contribution

It introduces a formal dynamical model of thalamocortical networks and demonstrates how feedback and feedforward control achieve selective inhibition and recruitment.

Findings

Thalamus enables failsafe mechanisms in brain networks.

Control requirements are reduced with thalamic involvement.

Networks show improved performance with thalamic regulation.

Abstract

Neuroscientific evidence shows that for most brain networks all pathways between cortical regions either pass through the thalamus or a transthalamic parallel route exists for any direct corticocortical connection. This paper seeks to formally study the dynamical behavior of the resulting thalamocortical brain networks with a view to characterizing the inhibitory role played by the thalamus and its benefits. We employ a linear-threshold mesoscale model for individual brain subnetworks and study both hierarchical and star-connected thalamocortical networks. Using tools from singular perturbation theory and switched systems, we show that selective inhibition and recruitment can be achieved in such networks through a combination of feedback and feedforward control. Various simulations throughout the exposition illustrate the benefits resulting from the presence of the thalamus regarding failsafe mechanisms, required control magnitude, and network performance.