O'Reilly R C, Rudy J W
Department of Psychology, University of Colorado at Boulder, 80309, USA.
Hippocampus. 2000;10(4):389-97. doi: 10.1002/1098-1063(2000)10:4<389::AID-HIPO5>3.0.CO;2-P.
We present an overview of our computational approach towards understanding the different contributions of the neocortex and hippocampus in learning and memory. The approach is based on a set of principles derived from converging biological, psychological, and computational constraints. The most central principles are that the neocortex employs a slow learning rate and overlapping distributed representations to extract the general statistical structure of the environment, while the hippocampus learns rapidly, using separated representations to encode the details of specific events while suffering minimal interference. Additional principles concern the nature of learning (error-driven and Hebbian), and recall of information via pattern completion. We summarize the results of applying these principles to a wide range of phenomena in conditioning, habituation, contextual learning, recognition memory, recall, and retrograde amnesia, and we point to directions of current development.
我们概述了我们的计算方法，旨在理解新皮层和海马体在学习与记忆中的不同作用。该方法基于一系列从生物学、心理学和计算限制中得出的原则。最核心的原则是，新皮层采用缓慢的学习速率和重叠的分布式表征来提取环境的一般统计结构，而海马体学习迅速，使用分离的表征来编码特定事件的细节，同时受到的干扰最小。其他原则涉及学习的本质（误差驱动和赫布式），以及通过模式完成来回忆信息。我们总结了将这些原则应用于条件作用、习惯化、情境学习、识别记忆、回忆和逆行性遗忘等广泛现象的结果，并指出了当前的发展方向。
