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从细胞类型到群体动态:使海马流形具有生理可解释性。

From cell types to population dynamics: Making hippocampal manifolds physiologically interpretable.

机构信息

Instituto Cajal, CSIC, Madrid 28012, Spain.

Instituto Cajal, CSIC, Madrid 28012, Spain.

出版信息

Curr Opin Neurobiol. 2023 Dec;83:102800. doi: 10.1016/j.conb.2023.102800. Epub 2023 Oct 26.

Abstract

The study of the hippocampal code is gaining momentum. While the physiological approach targets the contribution of individual cells as determined by genetic, biophysical and circuit factors, the field pushes for a population dynamic approach that considers the representation of behavioural variables by a large number of neurons. In this alternative framework, neuronal activity is projected into low-dimensional manifolds. These manifolds can reveal the structure of population representations, but their physiological interpretation is challenging. Here, we review the recent literature and propose that integrating information regarding behavioral traits, local field potential oscillations and cell-type-specificity into neural manifolds offers strategies to make them interpretable at the physiological level.

摘要

海马体代码的研究正在兴起。虽然生理方法的目标是确定遗传、生物物理和电路因素对单个细胞贡献,但该领域正在推动一种群体动态方法,该方法考虑了大量神经元对行为变量的表示。在这种替代框架中,神经元活动被投射到低维流形中。这些流形可以揭示群体表示的结构,但它们的生理学解释具有挑战性。在这里,我们回顾了最近的文献,并提出将关于行为特征、局部场电位振荡和细胞类型特异性的信息整合到神经流形中,可以提供在生理水平上解释它们的策略。

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