小柯机器人

海马中的场景编码依赖于经验
2021-03-25 13:07

美国斯坦福大学Lisa M. Giocomo和Mark H. Plitt合作发现,海马中的场景编码依赖于经验。2021年3月22日,《自然—神经科学》杂志在线发表了这项成果。

通过使用虚拟导航期间在小鼠中的大规模体内双光子细胞内钙记录,研究人员显示,海马区域CA1中的重映射是由有关某些场景频率的先前经验驱动的,并且重映射近似于对当前场景身份的最佳估计。一个简单的联想学习机制可以再现这些结果。

总之,这些发现表明,位置细胞重映射使动物能够同时识别其物理位置并以最佳方式估计场景身份。

据了解,海马包含能够支持陈述性记忆的神经表征。海马位置细胞就是这样一种代表,它在给定环境中的一个或几个位置放电。在环境之间,位置细胞放电场重新映射(打开/关闭或移动到新位置),从而为不同的场景提供群体范围的代码。但是,如何结合场景特征来驱动海马重映射仍然是一个有争议的问题。

附:英文原文

Title: Experience-dependent contextual codes in the hippocampus

Author: Mark H. Plitt, Lisa M. Giocomo

Issue&Volume: 2021-03-22

Abstract: The hippocampus contains neural representations capable of supporting declarative memory. Hippocampal place cells are one such representation, firing in one or few locations in a given environment. Between environments, place cell firing fields remap (turning on/off or moving to a new location) to provide a population-wide code for distinct contexts. However, the manner by which contextual features combine to drive hippocampal remapping remains a matter of debate. Using large-scale in vivo two-photon intracellular calcium recordings in mice during virtual navigation, we show that remapping in the hippocampal region CA1 is driven by prior experience regarding the frequency of certain contexts and that remapping approximates an optimal estimate of the identity of the current context. A simple associative-learning mechanism reproduces these results. Together, our findings demonstrate that place cell remapping allows an animal to simultaneously identify its physical location and optimally estimate the identity of the environment. 

DOI: 10.1038/s41593-021-00816-6

Source: https://www.nature.com/articles/s41593-021-00816-6

Nature Neuroscience:《自然—神经科学》,创刊于1998年。隶属于施普林格·自然出版集团,最新IF:28.771
官方网址:https://www.nature.com/neuro/
投稿链接:https://mts-nn.nature.com/cgi-bin/main.plex


本期文章:《自然—神经科学》:Online/在线发表

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