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通过同步多点光学采样进行皮层观测揭示了动作的广泛群体编码。

Cortical Observation by Synchronous Multifocal Optical Sampling Reveals Widespread Population Encoding of Actions.

机构信息

Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.

Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.

出版信息

Neuron. 2020 Jul 22;107(2):351-367.e19. doi: 10.1016/j.neuron.2020.04.023. Epub 2020 May 19.

Abstract

To advance the measurement of distributed neuronal population representations of targeted motor actions on single trials, we developed an optical method (COSMOS) for tracking neural activity in a largely uncharacterized spatiotemporal regime. COSMOS allowed simultaneous recording of neural dynamics at ∼30 Hz from over a thousand near-cellular resolution neuronal sources spread across the entire dorsal neocortex of awake, behaving mice during a three-option lick-to-target task. We identified spatially distributed neuronal population representations spanning the dorsal cortex that precisely encoded ongoing motor actions on single trials. Neuronal correlations measured at video rate using unaveraged, whole-session data had localized spatial structure, whereas trial-averaged data exhibited widespread correlations. Separable modes of neural activity encoded history-guided motor plans, with similar population dynamics in individual areas throughout cortex. These initial experiments illustrate how COSMOS enables investigation of large-scale cortical dynamics and that information about motor actions is widely shared between areas, potentially underlying distributed computations.

摘要

为了在单次试验中推进对靶向运动动作的分布式神经元群体表现的测量,我们开发了一种光学方法(COSMOS),用于在一个很大程度上未被描述的时空范围内跟踪神经活动。COSMOS 允许在清醒、行为小鼠进行三选项舔目标任务期间,从整个背外侧新皮层中超过 1000 个接近细胞分辨率的神经元源以约 30 Hz 的频率同时记录神经动力学。我们确定了跨越背侧皮层的空间分布式神经元群体表现,这些表现可以精确地对单个试验中的正在进行的运动动作进行编码。使用未平均的、整个会话数据在视频速率下测量的神经元相关性具有局部空间结构,而试验平均数据则表现出广泛的相关性。可分离的神经活动模式编码了基于历史的运动计划,个体区域的群体动力学相似。这些初步实验说明了 COSMOS 如何能够研究大规模皮质动力学,以及关于运动动作的信息在区域之间广泛共享,可能是分布式计算的基础。

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