A computational principle for hippocampal learning and neurogenesis
A computational principle for hippocampal learning and neurogenesis
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DOI：
10.1002/hipo.20095
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发表时间：
2005-01-01
期刊：
影响因子：
3.5
通讯作者：
Becker, S
中科院分区：
文献类型：
--
作者：
Becker, S
自马尔提出海马编码的计算理论以来的三十年里，许多计算模型都是基于马尔提出的相同的关键原则：稀疏表示，快速赫布存储，联想回忆和巩固。这些模型中的大多数都集中在CA 3或CA 1领域，使用“现成的”学习算法，如竞争学习或Hebbian模式关联。在这里，我们提出了一个新的编码原则，是共同的所有海马区，并从这一个原则，我们得出的学习规则，海马内的每个主要途径。学习规则与文献中的几个CA 3和CA 1模型有很多共同之处，并提供了一个统一的框架来查看这些模型。完整回路的模拟证实，识别记忆和回忆都上级脑损伤模型，与人类数据一致。此外，我们提出了在齿状回（DG）的神经发生的功能作用，即，创建高度相似的项目不同的记忆痕迹。我们的模拟结果支持我们的预测，即记忆容量随着齿状颗粒细胞的数量增加，而固定齿状层大小的神经元周转率通过最大限度地减少高度相似项目之间的干扰来提高回忆。(c)2005 Wiley-Liss，Inc.
In the three decades since Marr put forward his computational theory of hippocampal coding, many computational models have been built on the same key principles proposed by Marr: sparse representations, rapid Hebbian storage, associative recall and consolidation. Most of these models have focused on either the CA3 or CA1 fields, using "off-the-shelf" learning algorithms such as competitive learning or Hebbian pattern association. Here, we propose a novel coding principle that is common to all hippocampal regions, and from this one principal, we derive learning rules for each of the major pathways within the hippocampus. The learning rules turn out to have much in common with several models of CA3 and CA1 in the literature, and provide a unifying framework in which to view these models. Simulations of the complete circuit confirm that both recognition memory and recall are superior relative to a hippocampally lesioned model, consistent with human data. Further, we propose a functional role for neurogenesis in the dentate gyrus (DG), namely, to create distinct memory traces for highly similar items. Our simulation results support our prediction that memory capacity increases with the number of dentate granule cells, while neuronal turnover with a fixed dentate layer size improves recall, by minimizing interference between highly similar items. (c) 2005 Wiley-Liss, Inc.