How hippocampus and cortex contribute to recognition memory: revisiting the complementary learning systems model.
How hippocampus and cortex contribute to recognition memory: revisiting the complementary learning systems model.
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DOI：
10.1002/hipo.20855
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发表时间：
2010-11
期刊：
影响因子：
3.5
通讯作者：
Norman, Kenneth A.
中科院分区：
文献类型：
--
作者：
Norman, Kenneth A.
我们描述了识别记忆的互补学习系统神经网络模型如何阐明当前关于海马和皮层对识别记忆的贡献的争论。我们回顾了模拟结果，说明了海马体和皮层如何（根据模型）促进识别记忆的三个关键差异，所有这些差异都源于海马体对模式分离表征的使用。模式分离使得海马体特别适合于区分研究过的项目和相关的诱饵；这使得海马体特别不适合计算全局匹配；海马ROC曲线的y轴截距为> 0。我们还描述了这些差异的一个关键边界条件：当实验中项目之间的平均相似性水平非常高时，海马体模式分离可能会失败，此时海马体模型将开始表现得像皮层模型一样。我们描述了这些模拟结果对如何描述海马体与皮层的贡献以及如何测量这些贡献的影响。
We describe how the Complementary Learning Systems neural network model of recognition memory can shed light on current debates regarding hippocampal and cortical contributions to recognition memory. We review simulation results illustrating three critical differences in how (according to the model) hippocampus and cortex contribute to recognition memory, all of which derive from the hippocampus’ use of pattern separated representations. Pattern separation makes the hippocampus especially well-suited for discriminating between studied items and related lures; it makes the hippocampus especially poorly suited for computing global match; and it imbues the hippocampal ROC curve with a Y-intercept > 0. We also describe a key boundary condition on these differences: When the average level of similarity between items in an experiment is very high, hippocampal pattern separation can fail, at which point the hippocampal model will start to behave like the cortical model. We describe the implications of these simulation results for extant debates over how to describe hippocampal vs. cortical contributions and how to measure these contributions.
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