Oxford Centre for Computational Neuroscience, Oxford, England.
Department of Computer Science, University of Warwick, Coventry, England.
Cell Tissue Res. 2018 Sep;373(3):577-604. doi: 10.1007/s00441-017-2744-3. Epub 2017 Dec 7.
A quantitative computational theory of the operation of the hippocampus as an episodic memory system is described. The CA3 system operates as a single attractor or autoassociation network (1) to enable rapid one-trial associations between any spatial location (place in rodents or spatial view in primates) and an object or reward and (2) to provide for completion of the whole memory during recall from any part. The theory is extended to associations between time and object or reward to implement temporal order memory, which is also important in episodic memory. The dentate gyrus performs pattern separation by competitive learning to create sparse representations producing, for example, neurons with place-like fields from entorhinal cortex grid cells. The dentate granule cells generate, by the very small number of mossy fibre connections to CA3, a randomizing pattern separation effect that is important during learning but not recall and that separates out the patterns represented by CA3 firing as being very different from each other. This is optimal for an unstructured episodic memory system in which each memory must be kept distinct from other memories. The direct perforant path input to CA3 is quantitatively appropriate for providing the cue for recall in CA3 but not for learning. The CA1 recodes information from CA3 to set up associatively learned backprojections to the neocortex to allow the subsequent retrieval of information to the neocortex, giving a quantitative account of the large number of hippocampo-neocortical and neocortical-neocortical backprojections. Tests of the theory including hippocampal subregion analyses and hippocampal NMDA receptor knockouts are described and support the theory.
描述了一种作为情景记忆系统运作的海马体的定量计算理论。CA3 系统作为单一吸引子或自联想网络运作:(1) 实现了任何空间位置(啮齿动物的位置或灵长类动物的空间视图)与物体或奖励之间的快速单次联想;(2) 在回忆过程中从任何部分完成整个记忆。该理论扩展到时间和物体或奖励之间的联想,以实现时间顺序记忆,这在情景记忆中也很重要。齿状回通过竞争学习进行模式分离,创建稀疏表示,例如,从内嗅皮层网格细胞产生具有位置样场的神经元。通过苔藓纤维与 CA3 的极少数连接,齿状颗粒细胞产生了随机模式分离效应,这在学习过程中很重要,但在回忆过程中不重要,它将 CA3 放电所代表的模式彼此区分开来。这对于非结构化情景记忆系统是最优的,其中每个记忆必须与其他记忆区分开来。直接穿通通路到 CA3 的输入对于提供 CA3 中的回忆线索是定量合适的,但不适合学习。CA1 对 CA3 的信息进行重新编码,以建立与联想学习的后向投射到新皮层,允许随后向新皮层检索信息,从而对大量海马体-新皮层和新皮层-新皮层的后向投射进行定量解释。描述了对该理论的测试,包括海马体亚区分析和海马体 NMDA 受体敲除实验,并支持该理论。