Neural mechanisms of self-location.
Neural mechanisms of self-location.
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DOI：
10.1016/j.cub.2014.02.049
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发表时间：
2014-04-14
期刊：
影响因子：
9.2
通讯作者：
Burgess, N.
中科院分区：
文献类型：
--
作者：
Barry, C.;Burgess, N.
在复杂多变的环境中自我定位和导航到记忆目标的能力对许多移动的物种的生存至关重要。对哺乳动物海马和相关脑结构的电生理学研究已经确定了几类神经元，它们代表了生物体的位置和方向信息。这些细胞包括位置细胞、网格细胞、头部方向细胞和边界向量细胞，以及代表自我运动方面的细胞。了解这些神经表征是如何从环境感觉信息和与自我运动有关的信息中形成和更新的是一个重要的课题，吸引了相当多的当前兴趣。在这里，我们审查的计算机制，认为这些不同的空间表示，它们之间的相互作用，以及它们在指导行为的形成的基础。其中包括一些神经科学普遍感兴趣的计算机制的最清晰的例子，如吸引子动力学，时间编码和多模态集成。我们还讨论了计算建模和实验研究之间的密切关系，这是推动这一领域的进展。在复杂多变的环境中导航的能力对许多移动的物种的生存至关重要。巴里和伯吉斯审查的计算机制介导的位置，网格，头部方向和边界矢量细胞的空间表示形成和用于指导行为，并讨论了计算建模和实验研究在这一领域之间的密切关系。
The ability to self-localise and to navigate to remembered goals in complex and changeable environments is crucial to the survival of many mobile species. Electrophysiological investigations of the mammalian hippocampus and associated brain structures have identified several classes of neurons which represent information about an organism’s position and orientation. These include place cells, grid cells, head direction cells, and boundary vector cells, as well as cells representing aspects of self-motion. Understanding how these neural representations are formed and updated from environmental sensory information and from information relating to self-motion is an important topic attracting considerable current interest. Here we review the computational mechanisms thought to underlie the formation of these different spatial representations, the interactions between them, and their use in guiding behaviour. These include some of the clearest examples of computational mechanisms of general interest to neuroscience, such as attractor dynamics, temporal coding and multi-modal integration. We also discuss the close relationships between computational modelling and experimental research which are driving progress in this area. The ability to navigate in complex and changeable environments is crucial to the survival of many mobile species. Barry and Burgess review the computational mechanisms by which the spatial representations mediated by place, grid, head-direction and boundary-vector cells are formed and used to guide behavior, and discuss the close relationships between computational modelling and experimental research in this area.
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