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海马 CA3 网络在记忆功能中的定量理论。

A quantitative theory of the functions of the hippocampal CA3 network in memory.

机构信息

Oxford Centre for Computational Neuroscience Oxford, UK ; Department of Computer Science, University of Warwick Coventry, UK.

出版信息

Front Cell Neurosci. 2013 Jun 25;7:98. doi: 10.3389/fncel.2013.00098. eCollection 2013.

Abstract

A quantitative computational theory of the operation of the hippocampal CA3 system as an autoassociation or attractor network used in episodic memory system is described. In this theory, 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 (DG) performs pattern separation by competitive learning to produce sparse representations suitable for setting up new representations in CA3 during learning, 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 fiber (MF) 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 (pp) input to CA3 is quantitatively appropriate to provide the cue for recall in CA3, but not for learning. Tests of the theory including hippocampal subregion analyses and hippocampal NMDA receptor knockouts are described, and support the theory.

摘要

描述了一种海马 CA3 系统作为情景记忆系统中的自联想或吸引子网络运作的定量计算理论。在该理论中,CA3 系统作为单个吸引子或自联想网络运行,以实现任何空间位置(啮齿动物的位置或灵长类动物的空间视图)和物体或奖励之间的快速、单次试验关联,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(DG)通过竞争学习进行模式分离,以产生稀疏表示,适合在学习过程中在 CA3 中建立新表示,例如从内嗅皮层网格细胞产生具有位置样场的神经元。由于苔藓纤维(MF)与 CA3 的连接数量非常少,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 放电表示的模式彼此分离,彼此非常不同,这对于非结构化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。CA3 的直接穿通纤维(pp)输入定量适当,可以提供 CA3 中的回忆线索,但不能用于学习。描述了对该理论的测试,包括海马亚区分析和海马 NMDA 受体敲除,并且支持该理论。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b10/3691555/bbb497476ed6/fncel-07-00098-g0001.jpg

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