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, how timescales are defined and measured from brain recordings vary across the literature. Moreover, these observations do not specify the mechanisms underlying timescale variations, nor whether specific timescales are necessary for neural computation and brain function. Here, we synthesize three directions where computational approaches can distill the broad set of empirical observations into quantitative and testable theories: We review (i) how different data analysis methods quantify timescales across distinct behavioral states and recording modalities, (ii) how biophysical models provide mechanistic explanations for the emergence of diverse timescales, and (iii) how task-performing networks and machine learning models uncover the functional relevance of neural timescales. This integrative computational perspective thus complements experimental investigations, providing a holistic view on how neural timescales reflect the relationship between brain structure, dynamics, and behavior.

翻译：神经活动在大脑内部及不同脑区之间以广泛的时间尺度波动。实验观察表明，多样的神经时间尺度反映了动态环境中的信息。然而，文献中关于如何从脑记录中定义和测量时间尺度存在差异。此外，这些观察既未阐明时间尺度变化的潜在机制，也未证实特定时间尺度是否为神经计算和脑功能所必需。本文综合了三个研究方向，通过计算方法将广泛的实证观察提炼为可量化、可检验的理论：我们综述了（i）不同数据分析方法如何量化不同行为状态和记录模态下的时间尺度，（ii）生物物理模型如何为多样时间尺度的涌现提供机制性解释，以及（iii）任务执行网络与机器学习模型如何揭示神经时间尺度的功能相关性。这一整合的计算视角由此补充了实验研究，为理解神经时间尺度如何反映大脑结构、动力学与行为之间的关系提供了整体性框架。