Hierarchical Selective Recruitment in Linear-Threshold Brain Networks, Part I: Single-Layer Dynamics and Selective Inhibition
Erfan Nozari, Jorge Cort\'es

TL;DR
This paper introduces a control theory-based framework for understanding goal-driven selective attention in the brain, focusing on hierarchical inhibition and recruitment in linear-threshold neural networks.
Contribution
It develops a novel hierarchical model combining neuroscience insights with control theory, analyzing the dynamics and stability of selective inhibition mechanisms.
Findings
Conditions for equilibrium existence and stability in neural layers
Biologically-inspired inhibition schemes achieve selective suppression
Task-relevant subnetworks determine overall dynamical properties
Abstract
Goal-driven selective attention (GDSA) refers to the brain's function of prioritizing the activity of a task-relevant subset of its overall network to efficiently process relevant information while inhibiting the effects of distractions. Despite decades of research in neuroscience, a comprehensive understanding of GDSA is still lacking. We propose a novel framework using concepts and tools from control theory as well as insights and structures from neuroscience. Central to this framework is an information-processing hierarchy with two main components: selective inhibition of task-irrelevant activity and top-down recruitment of task-relevant activity. We analyze the internal dynamics of each layer of the hierarchy described as a network with linear-threshold dynamics and derive conditions on its structure to guarantee existence and uniqueness of equilibria, asymptotic stability, and…
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