Decomposing Thermodynamic Dissipation of Linear Langevin Systems via Oscillatory Modes and Its Application to Neural Dynamics

Abstract

Recent developments in stochastic thermodynamics have elucidated various relations between the entropy production rate (thermodynamic dissipation) and the physical limits of information processing in nonequilibrium dynamical systems. These findings have opened new perspectives in analyzing real biological systems. In neuroscience, the importance of quantifying entropy production has attracted attention for understanding information processing in the brain. However, the relationship between the entropy production rate and oscillations, which are common in many biological systems, remains unclear. For instance, neural oscillations like delta, theta, and alpha waves play crucial roles in brain information processing. Here, we derive a novel decomposition of the entropy production rate of linear Langevin systems. We show that one component of the entropy production rate, called the housekeeping entropy production rate, can be decomposed into independent positive contributions from oscillatory modes. Our decomposition enables us to calculate the contribution of oscillatory modes to the housekeeping entropy production rate. In addition, when the noise matrix is diagonal, the contribution of each oscillatory mode can be further decomposed into the contribution of each system element. To demonstrate the utility of our decomposition, we applied it to an electrocorticography (ECoG) dataset recorded during awake and anesthetized conditions in monkeys, where the oscillatory properties change drastically. We showed consistent trends across different monkeys: the contribution of delta band was larger in the anesthetized condition than in the awake condition, while those from higher frequency bands, such as the theta band, were smaller. These results allow us to interpret the changes in neural oscillation in terms of stochastic thermodynamics and the physical limits of information processing.

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