University of Utah, Department of Psychology, Salt Lake City, UT 84112, USA.
Oxford Centre for Computational Neuroscience, Oxford, UK; University of Warwick, Department of Computer Science, Coventry CV4 7AL, UK.
Neurosci Biobehav Rev. 2015 Jan;48:92-147. doi: 10.1016/j.neubiorev.2014.11.009. Epub 2014 Nov 20.
The aims of the paper are to update Rolls' quantitative computational theory of hippocampal function and the predictions it makes about the different subregions (dentate gyrus, CA3 and CA1), and to examine behavioral and electrophysiological data that address the functions of the hippocampus and particularly its subregions. Based on the computational proposal that the dentate gyrus produces sparse representations by competitive learning and via the mossy fiber pathway forces new representations on the CA3 during learning (encoding), it has been shown behaviorally that the dentate gyrus supports spatial pattern separation during learning. Based on the computational proposal that CA3-CA3 autoassociative networks are important for episodic memory, it has been shown behaviorally that the CA3 supports spatial rapid one-trial learning, learning of arbitrary associations where space is a component, pattern completion, spatial short-term memory, and spatial sequence learning by associations formed between successive items. The concept that the CA1 recodes information from CA3 and sets up associatively learned backprojections to neocortex to allow subsequent retrieval of information to neocortex, is consistent with findings on consolidation. Behaviorally, the CA1 is implicated in processing temporal information as shown by investigations requiring temporal order pattern separation and associations across time; and computationally this could involve associations in CA1 between object and timing information that have their origins in the lateral and medial entorhinal cortex respectively. The perforant path input from the entorhinal cortex to DG is implicated in learning, to CA3 in retrieval from CA3, and to CA1 in retrieval after longer time intervals ("intermediate-term memory") and in the temporal sequence memory for objects.
本文的目的是更新 Rolls 关于海马功能的定量计算理论及其对不同亚区（齿状回、CA3 和 CA1）的预测，并检验行为和电生理数据，这些数据涉及海马体的功能，特别是其亚区的功能。基于计算模型提出，齿状回通过竞争学习和通过苔藓纤维通路在学习（编码）期间将新的表示强加给 CA3，从而产生稀疏表示，行为研究表明齿状回在学习过程中支持空间模式分离。基于 CA3-CA3 自联想网络对情景记忆很重要的计算假设，行为研究表明 CA3 支持空间快速一次性学习、空间是组成部分的任意联想学习、模式完成、空间短期记忆以及通过连续项目之间形成的关联进行空间序列学习。CA1 从 CA3 重新编码信息并建立与新皮层的联想学习反向投射，以允许随后从新皮层检索信息的概念与巩固的发现一致。行为上，CA1 参与处理时间信息，这一点可以通过需要时间顺序模式分离和跨时间关联的研究来证明；从计算的角度来看，这可能涉及来自外侧和内侧内嗅皮层的对象和时间信息在 CA1 中的关联。来自内嗅皮层到齿状回的穿通路径输入与学习有关，与 CA3 有关的是从 CA3 检索，与 CA1 有关的是在较长时间间隔（“中期记忆”）后检索和对物体的时间序列记忆。
