Nonequilibrium physics of brain dynamics (2504.12188v1)

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.

An Overview of "Nonequilibrium physics of brain dynamics"

The paper "Nonequilibrium physics of brain dynamics" by Nartallo-Kaluarachchi et al. provides a comprehensive exploration into how nonequilibrium statistical physics can elucidate the complexities of neural dynamics. This paper represents an interdisciplinary synthesis, bridging concepts from physics, mathematics, and neuroscience to offer insights into the dynamic information processing capabilities of the brain. The authors propose that the brain operates in a state far from thermodynamic equilibrium, with such conditions playing a crucial role in cognitive complexity and consciousness.

To understand this, the paper explores the time-irreversibility and broken detailed balance observed in the brain's electrophysiological and neuroimaging data. It identifies entropy production as a marker of the brain's nonequilibrium state and a signature of its cognitive functions. This perspective challenges the traditional equilibrium-based models, emphasizing the importance of a nonequilibrium approach to capture the true nature of brain dynamics.

The paper is structured to first introduce the theoretical frameworks underpinning nonequilibrium dynamics. It then reviews methodologies for analyzing such dynamics—classified into model-based and model-free approaches. Model-based methods interpret neuroimaging and neural spike-train data by fitting nonequilibrium models to these signals, revealing insights into how the brain's dynamic states vary with cognitive tasks. Model-free techniques, on the other hand, provide measures of irreversibility indicative of nonequilibrium processes, as demonstrated by techniques analysing autocorrelations or employing machine learning to identify temporal irreversibility in neural recordings.

Particularly noteworthy is the implication that nonequilibrium dynamics might underlie various states of consciousness and cognitive tasks. The paper suggests that nonequilibrium signatures decrease during states of reduced consciousness, such as sleep or under anesthetic influence, and vary with task demands. This variability opens pathways to explore how different neural states relate to different levels of cognitive engagement and consciousness.

The paper concludes by forecasting future directions for research and application in neuroscience, urging the integration of nonequilibrium physics with existing neuroscientific models. By simulating brain activity through nonequilibrium processes, the authors suggest we can achieve more accurate models of functional connectivity and cognitive processes. Furthermore, the paper encourages the exploration of hierarchical structures and causal flows in brain dynamics, leveraging nonequilibrium phenomena as key analytical tools.

In summary, the paper argues for a paradigm shift in understanding brain dynamics, emphasizing the need to incorporate nonequilibrium statistical physics into neuroscientific research. By highlighting the intrinsic nonequilibrium nature of brain activity and its critical role in cognitive functions, the authors set a foundation for future studies to explore brain dynamics beyond conventional models, potentially paving the way for breakthroughs in neural computation and mental health diagnostics.