Institute of Neuroscience, Departments of Biology, Mathematics and Physics, University of Oregon, Eugene, United States.
Elife. 2022 Jul 6;11:e76577. doi: 10.7554/eLife.76577.
Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms and computational principles underlie such intricate temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. Recent research converges on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising via coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities as well as noise from mesoscopic feedback loops in regulating flexible behavior. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigation to fill these gaps.
自然主义动物行为在时间域中表现出惊人的复杂组织，其可变性至少来自三个来源：层次结构、上下文和随机。这种复杂的时间特征背后的神经机制和计算原理是什么？在这篇综述中，我们批判性地评估了现有的关于自然行为中时间变异性的这些来源的行为和神经生理学证据。最近的研究集中在基于吸引子神经网络和亚稳态动力学的时间变异性的新兴机制理论上，这种理论是通过中尺度神经回路之间的协调相互作用产生的。我们强调了结构异质性以及从中尺度反馈回路产生的噪声在调节灵活行为方面的关键作用。我们评估了当前理论和实验文献中的缺点和缺失环节，并提出了新的研究方向来填补这些空白。
