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大规模神经记录需要新的见解来将大脑与行为联系起来。

Large-scale neural recordings call for new insights to link brain and behavior.

作者信息

Urai Anne E, Doiron Brent, Leifer Andrew M, Churchland Anne K

机构信息

Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.

Cognitive Psychology Unit, Leiden University, Leiden, The Netherlands.

出版信息

Nat Neurosci. 2022 Jan;25(1):11-19. doi: 10.1038/s41593-021-00980-9. Epub 2022 Jan 3.

Abstract

Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. In the present review, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modeling frameworks to interpret these data, and argue that the interpretation of brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.

摘要

如今,神经科学家能够测量比以往更多神经元的活动,并且正面临着将这些全脑范围的神经记录与计算及行为联系起来的挑战。在本综述中,我们首先描述用于探测大规模脑活动的新兴工具和技术,以及在此类测量背景下表征行为的新方法。接下来,我们强调从各种模型系统中的大规模神经记录获得的见解,并认为其中一些见解对传统理论框架构成了挑战。最后,我们详细阐述用于解释这些数据的现有建模框架,并认为对全脑范围神经记录的解释需要可能取决于所需理解水平的新理论方法。神经记录和理论发展方面的这些进展将为我们对大脑理解的关键进展铺平道路。

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