Neural timescales from a computational perspective

Neural activity fluctuates over a wide range of timescales within and across brain areas. Experimental observations suggest that diverse neural timescales reflect information in dynamic environments. However, the definitions and measurements of timescales derived from brain recordings vary across the literature. Moreover, these observations do not specify the mechanisms that underlie variations in timescales or whether specific timescales are necessary for neural computation and brain function. Here we synthesize three directions in which computational approaches can distill the broad set of empirical observations into quantitative and testable theories. We review (1) how different data analysis methods quantify timescales across distinct behavioral states and recording modalities; (2) how biophysical models provide mechanistic explanations for the emergence of diverse timescales; and (3) how task-performing networks and machine learning models uncover the functional relevance of neural timescales. This integrative computational perspective complements experimental investigations, providing a holistic view of how neural timescales reflect the relationships among brain structure, dynamics and behavior.