Competition, stability, and functionality in excitatory-inhibitory neural circuits

TL;DR

This paper extends energy-based models to asymmetric excitatory-inhibitory neural circuits, revealing a game-theoretic structure and stability principles that enhance understanding of neural dynamics and cortical function.

Contribution

It introduces a novel game-energetic framework for asymmetric neural networks and applies stability principles to analyze E-I circuit regulation and cortical computation.

Findings

Revealed a game-theoretic interpretation of neural dynamics.

Applied stability principles to E-I networks.

Demonstrated cortical columns as contrast enhancers.

Abstract

Energy-based models have become a central paradigm for understanding computation and stability in both theoretical neuroscience and machine learning. However, the energetic framework typically relies on symmetry in synaptic or weight matrices - a constraint that excludes biologically realistic systems such as excitatory-inhibitory (E-I) networks. When symmetry is relaxed, the classical notion of a global energy landscape fails, leaving the dynamics of asymmetric neural systems conceptually unanchored. In this work, we extend the energetic framework to asymmetric firing rate networks, revealing an underlying game-theoretic structure for the neural dynamics in which each neuron is an agent that seeks to minimize its own energy. In addition, we exploit rigorous stability principles from network theory to study regulation and balancing of neural activity in E-I networks. We combine the novel game-energetic interpretation and the stability results to revisit standard frameworks in theoretical neuroscience, such as the Wilson-Cowan and lateral inhibition models. These insights allow us to study cortical columns of lateral inhibition microcircuits as contrast enhancer - with the ability to selectively sharpen subtle differences in the environment through hierarchical excitation-inhibition interplay. Our results bridge energetic and game-theoretic views of neural computation, offering a pathway toward the systematic engineering of biologically grounded, dynamically stable neural architectures.