Linear-Threshold Network Models for Describing and Analyzing Brain Dynamics

TL;DR

This paper reviews linear-threshold rate (LTR) network models as a versatile framework for understanding complex brain dynamics, including stability, multistability, oscillations, and chaos, with applications to attention, memory, and epilepsy.

Contribution

It provides a comprehensive overview of LTR dynamics, highlighting their ability to model diverse brain processes and demonstrating their stability, bifurcations, and oscillatory behaviors in neural systems.

Findings

LTR dynamics exhibit mono- and multi-stability, limit cycles, and chaos.

Stability and bifurcation analysis relate to brain functions like attention and memory.

LTR models can simulate seizure dynamics in epilepsy.

Abstract

Over the past two decades, an increasing array of control-theoretic methods have been used to study the brain as a complex dynamical system and better understand its structure-function relationship. This article provides an overview on one such family of methods, based on the linear-threshold rate (LTR) dynamics, which arises when modeling the spiking activity of neuronal populations and their impact on each other. LTR dynamics exhibit a wide range of behaviors based on network topologies and inputs, including mono- and multi-stability, limit cycles, and chaos, allowing it to be used to model many complex brain processes involving fast and slow inhibition, multiple time and spatial scales, different types of neural behavior, and higher-order interactions. Here we investigate how the versatility of LTR dynamics paired with concepts and tools from systems and control can provide a computational theory for explaining the dynamical mechanisms enabling different brain processes. Specifically, we illustrate stability and stabilization properties of LTR dynamics and how they are related to goal-driven selective attention, multistability and its relationship with declarative memory, and bifurcations and oscillations and their role in modeling seizure dynamics in epilepsy. We conclude with a discussion on additional properties of LTR dynamics and an outlook on other brain processess that for which they might be play a similar role.