Nonequilibrium physics of brain dynamics

The brain's neural activity, observed via electrophysiology and neuroimaging, shows time-irreversibility and broken detailed balance, indicating it operates in a nonequilibrium stationary state rather than a symmetric equilibrium. The degree of this nonequilibrium, measured by entropy production or irreversibility, appears to be a key marker of cognitive complexity and consciousness. This review introduces the mathematical frameworks for understanding nonequilibrium dynamics in continuous and discrete state-spaces, then surveys model-free and model-based analysis methods applied to neural recordings and spike-trains. It also touches on nonequilibrium computation in neural systems and concludes with a discussion of future directions for this emergent field at the intersection of nonequilibrium statistical physics and neuroscience.

Study at a glance

Abstract

Information processing in the brain is coordinated by the dynamic activity of neurons and neural populations at a range of spatiotemporal scales. These dynamics, captured in the form of electrophysiological recordings and neuroimaging, show evidence of time-irreversibility and broken detailed balance suggesting that the brain operates in a nonequilibrium stationary state. Furthermore, the level of nonequilibrium, measured by entropy production or irreversibility appears to be a crucial signature of cognitive complexity and consciousness. The subsequent study of neural dynamics from the perspective of nonequilibrium statistical physics is an emergent field that challenges the assumptions of symmetry and maximum-entropy that are common in traditional models. In this review, we discuss the plethora of exciting results emerging at the interface of nonequilibrium dynamics and neuroscience. We begin with an introduction to the mathematical paradigms necessary to understand nonequilibrium dynamics in both continuous and discrete state-spaces. Next, we review both model-free and model-based approaches to analysing nonequilibrium dynamics in both continuous-state recordings and neural spike-trains, as well as the results of such analyses. We briefly consider the topic of nonequilibrium computation in neural systems, before concluding with a discussion and outlook on the field.