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用于运动规划的神经算法和电路。

Neural Algorithms and Circuits for Motor Planning.

机构信息

Max Planck Florida Institute for Neuroscience, Jupiter, Florida, USA; email:

Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.

出版信息

Annu Rev Neurosci. 2022 Jul 8;45:249-271. doi: 10.1146/annurev-neuro-092021-121730. Epub 2022 Mar 22.

DOI:10.1146/annurev-neuro-092021-121730
PMID:35316610
Abstract

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.

摘要

大脑规划和执行意志运动。在非人类灵长类动物和啮齿动物的眼睛、四肢、舌头和头部运动的背景下,已经探索了神经群体活动的潜在模式。神经元网络如何产生准备特定运动的缓慢神经动力学以及最终启动这些运动的快速动力学?最近的工作利用快速和校准的神经活动干扰来测试能够产生观察到的神经活动的特定动力系统模型。这些联合的实验和计算研究表明,运动规划期间的皮质动力学反映了神经活动的固定点(吸引子)。皮质下控制信号在多个时间尺度上重塑和移动吸引子,导致对特定动作的承诺和快速过渡到运动执行。啮齿动物实验开始揭示这些算法如何在全脑神经回路的水平上实现。

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