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模型重建在核联想记忆中的作用。

Modeling reconsolidation in kernel associative memory.

机构信息

The Biologically Inspired Neural and Dynamic Systems (BINDS) Lab, Department of Computer Science, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America.

出版信息

PLoS One. 2013 Aug 2;8(8):e68189. doi: 10.1371/journal.pone.0068189. Print 2013.

Abstract

Memory reconsolidation is a central process enabling adaptive memory and the perception of a constantly changing reality. It causes memories to be strengthened, weakened or changed following their recall. A computational model of memory reconsolidation is presented. Unlike Hopfield-type memory models, our model introduces an unbounded number of attractors that are updatable and can process real-valued, large, realistic stimuli. Our model replicates three characteristic effects of the reconsolidation process on human memory: increased association, extinction of fear memories, and the ability to track and follow gradually changing objects. In addition to this behavioral validation, a continuous time version of the reconsolidation model is introduced. This version extends average rate dynamic models of brain circuits exhibiting persistent activity to include adaptivity and an unbounded number of attractors.

摘要

记忆再巩固是一种使适应性记忆和对不断变化的现实的感知成为可能的核心过程。它会导致记忆在被回忆后得到加强、减弱或改变。本文提出了一个记忆再巩固的计算模型。与 Hopfield 型记忆模型不同,我们的模型引入了数量不限的、可更新的吸引子,这些吸引子可以处理实值的、大的、现实的刺激。我们的模型复制了再巩固过程对人类记忆的三个特征性影响:增加联想、恐惧记忆的消除,以及跟踪和跟随逐渐变化的物体的能力。除了这种行为验证之外,还引入了再巩固模型的连续时间版本。该版本将表现出持续活动的大脑电路的平均率动力学模型扩展到包括适应性和数量不限的吸引子。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37c1/3732245/4a32825749fa/pone.0068189.g001.jpg

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