Worden R
Logica Cambridge Ltd, Cambridge, U.K.
Hippocampus. 1992 Apr;2(2):165-87. doi: 10.1002/hipo.450020208.
This paper describes a computational theory of spatial learning and navigation and its possible realization in the hippocampus. In the theory, mammals store memories of their geographical environment as a large number of independent fragments. A typical fragment denotes a few prominent landmarks in some region, their geometric relations, and their nongeometric properties, such as smells and visual cues. Navigation involves piecing together current sense data and relevant fragments to form a local map of the animal's surroundings; this is like solving a jigsaw puzzle. This computational model has been implemented in a computer program, whose performance is broadly consistent with observed levels of animal performance, and laboratory results, in spatial learning. Possible realizations of the model in animal brains are discussed. Unlike some neural net models of spatial learning, the model is strongly geometric, and uses special neural structures to store and manipulate two-dimensional vectors and bearings. A possible neural architecture is described in which the hippocampus performs the geometric operations; this has a long-term memory for fragments (somewhere in the neocortex), which can associatively recall fragments into a number of parallel fragment fitters, in the dentate gyrus and CA3 regions. These vary the positions and orientations of their fragments, to optimize the fit of the fragments to each other and to the animal's recent sense data. A local map of the animal's surroundings is stored in CA1 and subicular regions, where matching of fragment positions and attributes takes place. Mismatches are passed back via the entorhinal cortex to improve the fit during the next hippocampal theta cycle. The model offers the potential for understanding current data on spatial learning, on the neuroanatomy of the hippocampus and on place cells in a coherent framework, as well as understanding the role of the hippocampus in nonpositional memory tasks. Comparisons with experimental data are given.
本文描述了一种空间学习与导航的计算理论及其在海马体中可能的实现方式。在该理论中，哺乳动物将其地理环境的记忆存储为大量独立的片段。一个典型的片段表示某个区域中的一些显著地标、它们的几何关系以及它们的非几何属性，如气味和视觉线索。导航涉及将当前的感官数据与相关片段拼凑在一起，以形成动物周围环境的局部地图；这类似于解决一个拼图游戏。这个计算模型已在一个计算机程序中实现，其性能与观察到的动物在空间学习中的表现水平以及实验室结果大致相符。文中还讨论了该模型在动物大脑中的可能实现方式。与一些空间学习的神经网络模型不同，该模型具有很强的几何特性，并使用特殊的神经结构来存储和处理二维向量与方位。文中描述了一种可能的神经架构，其中海马体执行几何运算；它对片段具有长期记忆（在新皮层的某个地方），可以将片段联想式地回想起并输入到齿状回和CA3区域中的多个并行片段拟合器中。这些拟合器改变其片段的位置和方向，以优化片段之间以及与动物最近感官数据的匹配度。动物周围环境的局部地图存储在CA1和海马下托区域，在那里进行片段位置和属性的匹配。不匹配的情况会通过内嗅皮层反馈回去，以便在下一个海马体θ周期中改善匹配度。该模型为在一个连贯的框架内理解当前关于空间学习、海马体神经解剖学以及位置细胞的数据提供了可能性，同时也有助于理解海马体在非位置记忆任务中的作用。文中还给出了与实验数据的比较。
