Banquet J P, Gaussier Ph, Quoy M, Revel A, Burnod Y
INSERM U483 Neuroscience and Modelization, Université Pierre et Marie Curie, 75252 Paris, France.
Neural Comput. 2005 Jun;17(6):1339-84. doi: 10.1162/0899766053630369.
In this letter we describe a hippocampo-cortical model of spatial processing and navigation based on a cascade of increasingly complex associative processes that are also relevant for other hippocampal functions such as episodic memory. Associative learning of different types and the related pattern encoding-recognition take place at three successive levels: (1) an object location level, which computes the landmarks from merged multimodal sensory inputs in the parahippocampal cortices; (2) a subject location level, which computes place fields by combination of local views and movement-related information in the entorhinal cortex; and (3) a spatiotemporal level, which computes place transitions from contiguous place fields in the CA3-CA1 region, which form building blocks for learning temporospatial sequences. At the cell population level, superficial entorhinal place cells encode spatial, context-independent maps as landscapes of activity; populations of transition cells in the CA3-CA1 region encode context-dependent maps as sequences of transitions, which form graphs in prefrontal-parietal cortices. The model was tested on a robot moving in a real environment; these tests produced results that could help to interpret biological data. Two different goal-oriented navigation strategies were displayed depending on the type of map used by the system. Thanks to its multilevel, multimodal integration and behavioral implementation, the model suggests functional interpretations for largely unaccounted structural differences between hippocampo-cortical systems. Further, spatiotemporal information, a common denominator shared by several brain structures, could serve as a cognitive processing frame and a functional link, for example, during spatial navigation and episodic memory, as suggested by the applications of the model to other domains, temporal sequence learning and imitation in particular.
在这封信中，我们描述了一种基于一系列日益复杂的联想过程的海马体 - 皮质空间处理与导航模型，这些联想过程也与其他海马体功能（如情景记忆）相关。不同类型的联想学习以及相关的模式编码 - 识别发生在三个连续的层次上：（1）物体位置层次，它从海马旁皮质中合并的多模态感觉输入计算地标；（2）主体位置层次，它通过内嗅皮质中局部视图和与运动相关信息的组合计算位置场；（3）时空层次，它从CA3 - CA1区域中相邻的位置场计算位置转换，这些位置转换构成了学习时空序列的构建块。在细胞群体水平上，浅层内嗅位置细胞将空间、与上下文无关的地图编码为活动景观；CA3 - CA1区域中的转换细胞群体将与上下文相关的地图编码为转换序列，这些序列在前额叶 - 顶叶皮质中形成图形。该模型在真实环境中移动的机器人上进行了测试；这些测试产生的结果有助于解释生物学数据。根据系统使用的地图类型，展示了两种不同的目标导向导航策略。由于其多层次、多模态整合和行为实现，该模型为海马体 - 皮质系统之间很大程度上未得到解释的结构差异提供了功能解释。此外，时空信息是几种脑结构共有的一个共同特征，例如，正如该模型在其他领域（特别是时间序列学习和模仿）的应用所表明的那样，在空间导航和情景记忆过程中，它可以作为一种认知处理框架和功能联系。
