Frank Michael J, Rudy Jerry W, O'Reilly Randall C
Department of Psychology, University of Colorado, Boulder, Colorado 80309, USA.
Hippocampus. 2003;13(3):341-54. doi: 10.1002/hipo.10084.
A computational neural network model is presented that explains how the hippocampus can contribute to transitive inference performance observed in rats (Dusek and Eichenbaum, 1997. Proc Natl Acad Sci U S A 94:7109-7114; Van Elzakker et al., 2003. Hippocampus 12:this issue). In contrast to existing theories that emphasize the idea that the hippocampus contributes by flexibly relating previously encoded memories, we find that the hippocampus contributes by altering the elemental associative weights of individual stimulus elements during learning. We use this model to account for a range of existing data and to make a number of distinctive predictions that clearly contrast these two views.
本文提出了一种计算神经网络模型，该模型解释了海马体如何有助于大鼠的传递性推理表现（Dusek和Eichenbaum，1997年。《美国国家科学院院刊》94:7109 - 7114；Van Elzakker等人，2003年。《海马体》12:本期）。与现有理论强调海马体通过灵活关联先前编码的记忆来发挥作用不同，我们发现海马体在学习过程中通过改变单个刺激元素的基本关联权重来发挥作用。我们使用这个模型来解释一系列现有数据，并做出一些独特的预测，这些预测清楚地对比了这两种观点。
