Grossberg Stephen, Pilly Praveen K
Department of Mathematics, Center for Adaptive Systems, Graduate Program in Cognitive and Neural Systems, Center for Computational Neuroscience and Neural Technology, Department of Mathematics, Boston University, , 677 Beacon Street, Boston, MA 02215, USA.
Philos Trans R Soc Lond B Biol Sci. 2013 Dec 23;369(1635):20120524. doi: 10.1098/rstb.2012.0524. Print 2014 Feb 5.
A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model's parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC ('neural relativity'). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.
一种神经模型提出了内嗅网格细胞和海马位置细胞如何在自组织映射(SOM)层次结构中发展为空间类别。该模型通过学习具有多个空间尺度的六边形网格放电场的网格细胞以及具有一个或多个放电场的位置细胞来响应真实的大鼠导航轨迹,这些细胞与幼鼠中关于它们发育的神经生理学数据相匹配。网格细胞和位置细胞都可以通过检测、学习和记住其输入中最频繁和最活跃的共现情况来发育。该模型的简约特性包括:类似的环形吸引子机制处理驱动映射学习的线性和角向路径整合输入;相同的SOM机制可以学习网格细胞和位置细胞的感受野;并且通过内侧内嗅皮质到海马体(HC)的多个空间尺度模块的背腹组织的学习可能使用与通过外侧内嗅皮质到HC的时间学习相同的机制(“神经相对性”)。该模型阐明了自上而下的海马体到内嗅皮质的注意力机制如何稳定映射学习,模拟了海马体失活如何破坏网格细胞,并解释了关于theta、beta和伽马振荡的数据。本文还根据最近的数据比较了三种主要类型的网格细胞模型。