How entorhinal grid cells may learn multiple spatial scales from a dorsoventral gradient of cell response rates in a self-organizing map.
How entorhinal grid cells may learn multiple spatial scales from a dorsoventral gradient of cell response rates in a self-organizing map.
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DOI：
10.1371/journal.pcbi.1002648
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发表时间：
2012
影响因子：
4.3
通讯作者：
Pilly PK
中科院分区：
文献类型：
--
作者：
Grossberg S;Pilly PK
高等哺乳动物海马体中的位置细胞对空间导航至关重要。最近的模型阐明了这是如何通过内侧内嗅皮层（MEC）中的网格细胞如何输入位置细胞来实现的。网格细胞沿MEC背腹轴在多个空间尺度上呈现六边形网格放电模式。来自多个尺度网格细胞的信号自适应地结合在一起，激活比网格细胞代表更大空间的位置细胞。但是网格细胞是如何学会在形成六边形网格的多个位置发射的，并且沿着背腹轴增加空间尺度？内嗅第二层星状细胞的体外记录显示，阈下膜电位振荡（MPOs）的时间周期和兴奋性突触后电位（EPSPs）的时间常数都沿着这条轴增加。更慢（更快）的亚阈值mpo和更慢（更快）的epsp与更大（更小）的网格间距和场宽度相关。一个自组织映射神经模型解释了网格空间尺度的解剖梯度是如何被细胞学习的，这些细胞沿着梯度对来自多个尺度的条纹细胞的输入的反应更慢，这些细胞执行线性速度路径积分。模型细胞也表现出MPO频率随其反应速率而变化。因此，内在节律的梯度并不是振荡干扰作为网格细胞放电机制的有力证据。响应速率梯度与具有标准化接受野的输入条纹细胞相结合，可以沿MEC背腹轴重现所有已知的网格细胞的空间和时间特性。这种空间梯度机制与侧内嗅皮层及其海马突起的时间学习梯度机制是同源的。因此，空间表征和时间表征可能产生于同源机制，从而体现了一种机制上的“神经相对性”，这可能阐明情景记忆是如何习得的。空间导航是所有高等哺乳动物的一项关键能力，海马体中的位置细胞代表了它们导航的大空间。最近的模型阐明了这是如何通过内侧内嗅皮层（MEC）的网格细胞和位置细胞之间的相互作用发生的。网格细胞在空间上呈现六边形网格放电模式，并沿MEC的背腹轴呈多个空间尺度增加。来自多个尺度的网格细胞的信号结合起来，激活了比网格细胞代表更大空间的位置细胞。这篇文章展示了细胞反应率沿背腹轴的梯度如何使网格细胞的学习与观察到的空间尺度梯度作为动物导航的现实轨迹。观察到的栅格细胞膜电位振荡频率梯度是响应率梯度的直接结果。这种空间学习的梯度机制与内嗅外侧皮层及其海马体投射的时间学习梯度机制是同源的，从而阐明了为什么内嗅-海马体系统同时存在空间表征和时间表征。
Place cells in the hippocampus of higher mammals are critical for spatial navigation. Recent modeling clarifies how this may be achieved by how grid cells in the medial entorhinal cortex (MEC) input to place cells. Grid cells exhibit hexagonal grid firing patterns across space in multiple spatial scales along the MEC dorsoventral axis. Signals from grid cells of multiple scales combine adaptively to activate place cells that represent much larger spaces than grid cells. But how do grid cells learn to fire at multiple positions that form a hexagonal grid, and with spatial scales that increase along the dorsoventral axis? In vitro recordings of medial entorhinal layer II stellate cells have revealed subthreshold membrane potential oscillations (MPOs) whose temporal periods, and time constants of excitatory postsynaptic potentials (EPSPs), both increase along this axis. Slower (faster) subthreshold MPOs and slower (faster) EPSPs correlate with larger (smaller) grid spacings and field widths. A self-organizing map neural model explains how the anatomical gradient of grid spatial scales can be learned by cells that respond more slowly along the gradient to their inputs from stripe cells of multiple scales, which perform linear velocity path integration. The model cells also exhibit MPO frequencies that covary with their response rates. The gradient in intrinsic rhythmicity is thus not compelling evidence for oscillatory interference as a mechanism of grid cell firing. A response rate gradient combined with input stripe cells that have normalized receptive fields can reproduce all known spatial and temporal properties of grid cells along the MEC dorsoventral axis. This spatial gradient mechanism is homologous to a gradient mechanism for temporal learning in the lateral entorhinal cortex and its hippocampal projections. Spatial and temporal representations may hereby arise from homologous mechanisms, thereby embodying a mechanistic “neural relativity” that may clarify how episodic memories are learned. Spatial navigation is a critical competence of all higher mammals, and place cells in the hippocampus represent the large spaces in which they navigate. Recent modeling clarifies how this may occur via interactions between grid cells in the medial entorhinal cortex (MEC) and place cells. Grid cells exhibit hexagonal grid firing patterns across space and come in multiple spatial scales that increase along the dorsoventral axis of MEC. Signals from multiple scales of grid cells combine to activate place cells that represent much larger spaces than grid cells. This article shows how a gradient of cell response rates along the dorsoventral axis enables the learning of grid cells with the observed gradient of spatial scales as an animal navigates realistic trajectories. The observed gradient of grid cell membrane potential oscillation frequencies is shown to be a direct result of the gradient of response rates. This gradient mechanism for spatial learning is homologous to a gradient mechanism for temporal learning in the lateral entorhinal cortex and its hippocampal projections, thereby clarifying why both spatial and temporal representations are found in the entorhinal-hippocampal system.
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