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海马体认知表象中路径整合的起源和作用:对悬而未决问题的计算洞察。

Origin and role of path integration in the cognitive representations of the hippocampus: computational insights into open questions.

机构信息

The Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA

The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.

出版信息

J Exp Biol. 2019 Feb 6;222(Pt Suppl 1):jeb188912. doi: 10.1242/jeb.188912.

Abstract

Path integration is a straightforward concept with varied connotations that are important to different disciplines concerned with navigation, such as ethology, cognitive science, robotics and neuroscience. In studying the hippocampal formation, it is fruitful to think of path integration as a computation that transforms a sense of motion into a sense of location, continuously integrated with landmark perception. Here, we review experimental evidence that path integration is intimately involved in fundamental properties of place cells and other spatial cells that are thought to support a cognitive abstraction of space in this brain system. We discuss hypotheses about the anatomical and computational origin of path integration in the well-characterized circuits of the rodent limbic system. We highlight how computational frameworks for map-building in robotics and cognitive science alike suggest an essential role for path integration in the creation of a new map in unfamiliar territory, and how this very role can help us make sense of differences in neurophysiological data from novel versus familiar and small versus large environments. Similar computational principles could be at work when the hippocampus builds certain non-spatial representations, such as time intervals or trajectories defined in a sensory stimulus space.

摘要

路径整合是一个具有多种含义的简单概念,对于涉及导航的不同学科(如动物行为学、认知科学、机器人学和神经科学)非常重要。在研究海马体结构时,将路径整合视为一种将运动感知转化为位置感知的计算方法,与地标感知不断融合,这是很有成效的。在这里,我们回顾了实验证据,证明路径整合与被认为支持该脑系统中空间认知抽象的位置细胞和其他空间细胞的基本特性密切相关。我们讨论了关于在啮齿动物边缘系统的特征明确的回路中路径整合的解剖学和计算起源的假说。我们强调了机器人学和认知科学中的地图构建计算框架如何暗示路径整合在创建新的陌生领域地图方面的重要作用,以及这种作用如何帮助我们理解来自陌生和熟悉环境以及小环境和大环境的神经生理学数据之间的差异。当海马体构建某些非空间表示形式(例如在感觉刺激空间中定义的时间间隔或轨迹)时,类似的计算原理可能会起作用。

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