从计算视角理解神经时间尺度

Subjects

Abstract

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.

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Acknowledgements

This work was supported by the German Research Foundation (DFG) through Germany’s Excellence Strategy (EXC-2064/1, PN 390727645 to R.Z., J.H.M. and R.G.) and SFB1233 (PN 276693517 to J.H.M.), the Sofja Kovalevskaja Award from the Alexander von Humboldt Foundation endowed by the Federal Ministry of Education and Research (to R.Z. and A.L.), the Max Planck Society (to R.Z.), the European Union (ERC, ‘DeepCoMechTome’, grant 101089288 to J.H.M.), the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie (grant 101030918 (AutoMIND) to R.G.) and an add-on fellowship from the Joachim Herz Foundation (to R.Z.). The authors thank A. Manea and J. Zimmermann for providing the timescales of the fMRI data (Fig. 2a), as well as M. Chini, B. Voytek and the speakers and participants of the Cosyne 2022 workshop on ‘Mechanisms, functions and methods for diversity of neuronal and network timescales’ for valuable discussions.

Author information

These authors contributed equally: Roxana Zeraati, Richard Gao.

Authors and Affiliations

Max Planck Institute for Biological Cybernetics, Tübingen, Germany

Self-Organization and Optimality in Neuronal Networks, University of Tübingen, Tübingen, Germany

Bernstein Center for Computational Neuroscience Tübingen, Tübingen, Germany

Machine Learning in Science, University of Tübingen, Tübingen, Germany

Max Planck Institute for Intelligent Systems, Tübingen, Germany

Institute of Computer Science, Goethe University Frankfurt, Frankfurt, Germany

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R.Z. and R.G. conceptualized the overall structure of the study, wrote the first draft of the manuscript and prepared the figures. All authors discussed, revised and wrote the final version of the manuscript.

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Zeraati, R., Levina, A., Macke, J.H. et al. Neural timescales from a computational perspective. Nat Neurosci (2026). https://doi.org/10.1038/s41593-026-02343-8

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