Neurocomputing
Neurocomputing
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DOI：
10.1016/s0925-2312(00)00204-6
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发表时间：
2000-06
期刊：
影响因子：
6
通讯作者：
R. Hecht-Nielsen
中科院分区：
文献类型：
--
作者：
R. Hecht-Nielsen
我们研究如何在训练过程中的内嗅皮层输入的强度影响学习性能使用的最小计算模型的海马CA 3区的计算机模拟。在模型学习了两个部分重叠的序列之后，它在两个矛盾的预测问题上进行了测试-消除歧义和目标寻找。相对于总的活动，在学习过程中的内嗅输入的活动水平深刻地影响每个任务的表现。两个序列预测问题的最佳输入水平不同，但存在一个小的重叠区域，通常可以成功地执行这两个任务。这种对相对输入活动的敏感性表明了对模型的关键检验。
We investigate how the strength of entorhinal cortical inputs during training affects learned performance using computer simulations of a minimal computational model of hippocampal region CA3. After the model learns two partially overlapping sequences, it is tested on two contradictory prediction problems — disambiguation and goal-finding. Relative to total activity, the activity level of entorhinal inputs during learning profoundly affects performance on each task. The optimal input levels differ for the two sequence prediction problems, but a small region of overlap exists where both tasks can usually be performed successfully. This sensitivity to relative input activity suggests critical tests of the model.