首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Space, time and learning in the hippocampus: how fine spatial and temporal scales are expanded into population codes for behavioral control.
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Space, time and learning in the hippocampus: how fine spatial and temporal scales are expanded into population codes for behavioral control.

机译:海马的空间,时间和学习:如何将精细的时空尺度扩展为行为控制的人口代码。

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The hippocampus participates in multiple functions, including spatial navigation, adaptive timing and declarative (notably, episodic) memory. How does it carry out these particular functions? The present article proposes that hippocampal spatial and temporal processing are carried out by parallel circuits within entorhinal cortex, dentate gyrus and CA3 that are variations of the same circuit design. In particular, interactions between these brain regions transform fine spatial and temporal scales into population codes that are capable of representing the much larger spatial and temporal scales that are needed to control adaptive behaviors. Previous models of adaptively timed learning propose how a spectrum of cells tuned to brief but different delays are combined and modulated by learning to create a population code for controlling goal-oriented behaviors that span hundreds of milliseconds or even seconds. Here it is proposed how projections from entorhinal grid cells can undergo a similar learning process to create hippocampal place cells that can cover a space of many meters that are needed to control navigational behaviors. The suggested homology between spatial and temporal processing may clarify how spatial and temporal information may be integrated into an episodic memory. The model proposes how a path integration process activates a spatial map of grid cells. Path integration has a limited spatial capacity, and must be reset periodically, leading to the observed grid cell periodicity. Integration-to-map transformations have been proposed to exist in other brain systems. These include cortical mechanisms for numerical representation in the parietal cortex. As in the grid-to-place cell spatial expansion, the analog representation of number is extended by additional mechanisms to represent much larger numbers. The model also suggests how visual landmarks may influence grid cell activities via feedback projections from hippocampal place cells to the entorhinal cortex.
机译:海马参与多种功能,包括空间导航,自适应定时和陈述性(特别是情节性)记忆。它如何执行这些特定功能?本文提出,海马的时空处理是由内嗅皮层,齿状回和CA3内的平行回路进行的,这些回路是同一回路设计的变体。特别是,这些大脑区域之间的交互将精细的时空尺度转换为人口代码,该人口代码能够表示控制适应性行为所需的更大的时空尺度。以前的自适应定时学习模型提出了如何通过学习创建一个用于控制跨越数百毫秒甚至几秒钟的面向目标的行为的种群代码来组合和调制调谐到短暂但不同的延迟的一系列小区。在此提出了来自内脏网格细胞的投影如何经历相似的学习过程以创建海马位置细胞的方法,该细胞可以覆盖控制导航行为所需的许多米的空间。空间和时间处理之间建议的同源性可以阐明如何将空间和时间信息整合到情节记忆中。该模型提出了路径整合过程如何激活网格单元的空间图。路径积分的空间容量有限,必须定期重置,从而导致观察到的网格单元周期性。已经提出在其他大脑系统中存在积分到地图的转换。这些包括用于顶叶皮层中数字表示的皮层机制。就像在网格到位置单元的空间扩展中一样,数字的模拟表示通过其他机制扩展以表示更大的数字。该模型还建议视觉界标如何通过从海马体细胞到内嗅皮层的反馈投影影响网格细胞活动。

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