A nonequilibrium-potential approach to competition in neural populations

N. MARTINEZ; J. I. DEZA; R. R. DEZA

Palma de Mayorca

Conferencia; VIII GEFENOL; 2018

Building a theoretical framework for systems far from equilibrium constitutes a longstanding goal in Physics, with important implications for related disciplines. For the case of mathematical models of neurons and other excitable systems, the nonequilibrium potential approach proposed by Graham and colleagues has provided an interesting ground in this direction, although their applicability has been restricted to small neural circuits up to now. In this work, we 8 analytically obtain a nonequilibrium potential function for two classes of Wilson?Cowan rate models resembling a typical neocortical population, constituted by excitatory and inhibitory neurons. This function yields a nonequilibrium energy landscape of low dimensionality, providing a useful tool to understand the dynamics of large neural networks. We then (provisional) use the nonequilibrium potential to reproduce experimental evidence concerning the multistable neural dynamics associated with short-term memory and the nature of neural oscillations at the population level.