Timescales of neural activity are diverse across and within brain areas, and experimental observations suggest that neural timescales reflect information in dynamic environments. However, these observations do not specify how neural timescales are shaped, nor whether particular timescales are necessary for neural computations and brain function. Here, we take a complementary perspective and synthesize three directions where computational methods can distill the broad set of empirical observations into quantitative and testable theories: We review (i) how data analysis methods allow us to capture different timescales of neural dynamics across different recording modalities, (ii) how computational models provide a mechanistic explanation for the emergence of diverse timescales, and (iii) how task-optimized models in machine learning uncover the functional relevance of neural timescales. This integrative computational approach, combined with empirical findings, would provide a more holistic understanding of how neural timescales capture the relationship between brain structure, dynamics, and behavior.

翻译：神经活动的时间尺度在不同脑区之间及内部均呈现多样性，实验观测表明神经时间尺度反映了动态环境中的信息。然而，这些观测并未阐明神经时间尺度如何形成，亦未证实特定时间尺度是否为神经计算与脑功能所必需。本文采取一种互补性视角，综合了三个研究方向，旨在通过计算方法将广泛的实证观察提炼为可量化检验的理论：我们综述了（i）数据分析方法如何帮助我们从不同记录模态中捕捉神经动力学的多时间尺度特征，（ii）计算模型如何为多样化时间尺度的涌现提供机制性解释，以及（iii）机器学习中的任务优化模型如何揭示神经时间尺度的功能相关性。这种整合的计算方法，结合实证发现，将为理解神经时间尺度如何刻画脑结构、动力学与行为之间的关系提供更全面的视角。