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新皮层中的层次时间尺度:数学机制和生物学见解。

Hierarchical timescales in the neocortex: Mathematical mechanism and biological insights.

机构信息

School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China;

Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.

出版信息

Proc Natl Acad Sci U S A. 2022 Feb 8;119(6). doi: 10.1073/pnas.2110274119.

Abstract

A cardinal feature of the neocortex is the progressive increase of the spatial receptive fields along the cortical hierarchy. Recently, theoretical and experimental findings have shown that the temporal response windows also gradually enlarge, so that early sensory neural circuits operate on short timescales whereas higher-association areas are capable of integrating information over a long period of time. While an increased receptive field is accounted for by spatial summation of inputs from neurons in an upstream area, the emergence of timescale hierarchy cannot be readily explained, especially given the dense interareal cortical connectivity known in the modern connectome. To uncover the required neurobiological properties, we carried out a rigorous analysis of an anatomically based large-scale cortex model of macaque monkeys. Using a perturbation method, we show that the segregation of disparate timescales is defined in terms of the localization of eigenvectors of the connectivity matrix, which depends on three circuit properties: 1) a macroscopic gradient of synaptic excitation, 2) distinct electrophysiological properties between excitatory and inhibitory neuronal populations, and 3) a detailed balance between long-range excitatory inputs and local inhibitory inputs for each area-to-area pathway. Our work thus provides a quantitative understanding of the mechanism underlying the emergence of timescale hierarchy in large-scale primate cortical networks.

摘要

大脑新皮层的一个主要特征是,随着皮质层次的上升,空间感受野逐渐增大。最近的理论和实验研究结果表明,时间响应窗口也逐渐扩大,因此早期的感觉神经回路在短时间尺度上运作,而更高阶的联合区域则能够在较长时间内整合信息。虽然感受野的增大可以通过来自上游区域神经元的输入的空间总和来解释,但时间尺度层次的出现却不能轻易解释,特别是考虑到现代连接组中已知的密集的皮质间连接。为了揭示所需的神经生物学特性,我们对猕猴的基于解剖结构的大规模皮质模型进行了严格的分析。使用微扰方法,我们表明,不同时间尺度的分离是根据连接矩阵的特征向量的定位来定义的,这取决于三个电路特性:1)突触兴奋的宏观梯度,2)兴奋性和抑制性神经元群体之间不同的电生理特性,以及 3)每个区域间通路的长程兴奋性输入和局部抑制性输入之间的精细平衡。因此,我们的工作为大尺度灵长类皮质网络中时间尺度层次出现的机制提供了定量理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c7f/8832993/50167bc2a763/pnas.2110274119fig01.jpg

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