Conjunctive representations in learning and memory: principles of cortical and hippocampal function.
Conjunctive representations in learning and memory: principles of cortical and hippocampal function.
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DOI：
10.1037/0033-295x.108.2.311
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发表时间：
2001-04
影响因子：
5.4
通讯作者：
R. O’Reilly;J. Rudy
中科院分区：
文献类型：
--
作者：
R. O’Reilly;J. Rudy
作者提出了一个理论框架，了解海马和新皮层在学习和记忆中的作用。这个框架结合了许多海马功能理论中的一个主题：海马负责发展将刺激元素结合在一起的联合表征，使其成为一个单一的表征，以后可以从部分输入线索中回忆起来。这一想法与以下事实相矛盾：脑损伤的大鼠可以学习需要合取表征的非线性辨别问题。作者的框架通过建立一个原则性的劳动分工来适应这一发现，其中皮层负责缓慢学习，整合多种经验以提取概括性，而海马体则负责快速学习个体经验的任意内容。这个框架表明，涉及快速，偶然的联合学习的任务是更好的测试海马功能。作者在计算神经网络模型中实现了这个框架，并表明它可以解释动物学习中的各种数据。
The authors present a theoretical framework for understanding the roles of the hippocampus and neocortex in learning and memory. This framework incorporates a theme found in many theories of hippocampal function: that the hippocampus is responsible for developing conjunctive representations binding together stimulus elements into a unitary representation that can later be recalled from partial input cues. This idea is contradicted by the fact that hippocampally lesioned rats can learn nonlinear discrimination problems that require conjunctive representations. The authors' framework accommodates this finding by establishing a principled division of labor, where the cortex is responsible for slow learning that integrates over multiple experiences to extract generalities whereas the hippocampus performs rapid learning of the arbitrary contents of individual experiences. This framework suggests that tasks involving rapid, incidental conjunctive learning are better tests of hippocampal function. The authors implement this framework in a computational neural network model and show that it can account for a wide range of data in animal learning.