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Recent developments in stochastic thermodynamics have elucidated variousrelations between the entropy production rate (thermodynamic dissipation) andthe physical limits of information processing in nonequilibrium dynamicalsystems, which have been actively used and opened new perspectives in theanalysis of real biological systems. Even in neuroscience, the importance ofquantifying the entropy production has attracted increasing attention tounderstand the properties of information processing in the brain. However, therelationships between entropy production rate and neural oscillations, such asdelta, theta, and alpha waves, which are prevalent in the brain, are unclear.Here, we derive a novel decomposition of the entropy production rate. We showthat one of the components of the entropy production rate, called thehousekeeping entropy production rate, can be decomposed into independentpositive contributions from spatio-temporal oscillatory modes. Ourdecomposition enables us to calculate the contribution to the housekeepingentropy production rate from oscillatory modes, as well as the spatialdistribution of the contributions. To demonstrate the utility of ourdecomposition, we applied our decomposition to the electrocorticography (ECoG)dataset recorded during awake and anesthetized conditions in monkeys, where theproperties of oscillations change drastically. We showed the consistent trendsacross different monkeys, i.e., the contributions of oscillatory modes from thedelta band were larger in the anesthetized condition than in the awakecondition, while those from the higher frequency bands, such as the theta band,were smaller. These results allow us to interpret the change in the neuraloscillation in terms of stochastic thermodynamics and the physical limit ofinformation processing.

Decomposing Thermodynamic Dissipation of Neural Dynamics via Spatio-Temporal Oscillatory Modes