海马体中情景记忆形成的计算理论。
A computational theory of episodic memory formation in the hippocampus.
机构信息
Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.
出版信息
Behav Brain Res. 2010 Dec 31;215(2):180-96. doi: 10.1016/j.bbr.2010.03.027. Epub 2010 Mar 20.
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 to enable rapid, one-trial associations between any spatial location (place in rodents or spatial view in primates) and an object or reward and 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, also important in episodic memory. The dentate gyrus performs pattern separation by competitive learning to produce sparse representations, producing for example neurons with place-like fields from entorhinal cortex grid cells. The dentate granule cells produce by the very small number of mossy fibre connections to CA3 a randomizing pattern separation effect important during learning but not recall that separates out the patterns represented by CA3 firing to be very different from each other, which 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 to provide the cue for recall in CA3, but not for learning. The CA1 recodes information from CA3 to set up associatively learned backprojections to neocortex to allow subsequent retrieval of information to neocortex, providing 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 系统作为一个单一的吸引子或自联想网络运作,能够在任何空间位置(啮齿动物的位置或灵长类动物的空间视图)和物体或奖励之间快速进行单次关联,并在从任何部分回忆时完成整个记忆。该理论扩展到时间和物体或奖励之间的关联,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,产生稀疏表示,例如从内嗅皮层网格细胞产生具有位置样场的神经元。齿状回颗粒细胞通过非常少的苔藓纤维连接到 CA3 产生随机模式分离效应,这在学习过程中很重要,但在回忆过程中不重要,它将 CA3 放电所代表的模式分离出来,彼此之间非常不同,这对于非结构化的情景记忆系统是最优的,在该系统中,每个记忆都必须与其他记忆区分开来。直接的穿通纤维输入到 CA3 的数量是适当的,为 CA3 中的回忆提供线索,但不适合学习。CA1 从 CA3 重新编码信息,以建立与新皮层的联想性学习后向投射,从而允许随后从新皮层检索信息,为大量海马体-新皮层和新皮层-新皮层的后向投射提供了定量解释。描述并支持了该理论的测试,包括海马体亚区分析和海马体 NMDA 受体敲除。