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预测CA3的序列是一种灵活的关联器,它学习并利用上下文来解决类似海马体的任务。

A sequence predicting CA3 is a flexible associator that learns and uses context to solve hippocampal-like tasks.

作者信息

Levy W B

机构信息

Department of Neurological Surgery, University of Virginia Health Sciences Center, Charlottesville 22908, USA.

出版信息

Hippocampus. 1996;6(6):579-90. doi: 10.1002/(SICI)1098-1063(1996)6:6<579::AID-HIPO3>3.0.CO;2-C.

Abstract

The model discussed in this paper is, by hypothesis, a minimal, biologically plausible model of hippocampal region CA3. Because cognitive mapping can be viewed as a sequence prediction problem, we qualify this model as a successful sequence predictor. Since the model solves problems which require the use of context, the model is also able to learn and use context. The model also solves configural learning problems of which, at least one, requires a hippocampus. Thus, by solving sequence problems, by solving configural learning problems, and by creating codes for context, this model provides a computational unification of hippocampal functions which are often viewed as disparate.

摘要

根据假设,本文所讨论的模型是海马体CA3区的一个最小化、具有生物学合理性的模型。由于认知地图可被视为一个序列预测问题,我们将此模型认定为一个成功的序列预测器。因为该模型解决了需要运用上下文的问题,所以它也能够学习并运用上下文。该模型还解决了构型学习问题,其中至少有一个问题需要海马体参与。因此,通过解决序列问题、构型学习问题以及创建上下文编码,此模型为通常被视为互不相关的海马体功能提供了一种计算上的统一。

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