由动态上下文控制的内存访问门。

IF 2 4区 生物学 Q2 BIOLOGY
Andrés Pomi , Juan Lin , Eduardo Mizraji
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引用次数: 0

摘要

在日常生活中,获取记忆内容的暂时困难是一种常见的经历,例如,当我们试图在一个不寻常的环境中认出一个已知的人时。此外,最近的实验似乎表明,阿尔茨海默氏症早期的逆行性遗忘症是由于在获取正常安装的记忆时出现了障碍。这些事实表明,在刺激到达和联想识别之间存在一个中间环节。在这项工作中,提出了一个多模态神经计算模型,假定存在一个神经门,控制刺激物及其背景进入巩固记忆。如果没有实现识别,就会在由初始语境唤起的语境网络中启动随机搜索。这种搜索一直持续到找到可以识别的适当语境,或者由于工作记忆中不再保留初始刺激物而关闭搜索过程。该模型基于神经活动的矢量模式和与语境相关的矩阵记忆。我们将通过简单的马尔可夫链模拟来举例说明情境网络中可能出现的搜索情况。最后,我们将讨论该模型的一些特点和所研究的现象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A memory access gate controlled by dynamic contexts

Temporary difficulties in accessing the contents of memories are a common experience in everyday life, for example, when we try to recognize a known person in an unusual context. In addition, recent experiments seem to indicate that retrograde amnesia in the early stages of Alzheimer's disease is due to disorders in accessing memories that were installed normally. These facts suggest the existence of an intermediate step between the stimulus arrival and the associative recognition. In this work, a multimodular neurocomputational model is presented postulating the existence of a neural gate that controls the access of the stimulus with its context to the consolidated memory. If recognition is not achieved, a random search is initiated in a contextual network aroused by the initial context. The search continues until the appropriate context that allows for recognition is found or until the process is turned off because the initial stimulus is no longer maintained in the working memory. The model is based on vector patterns of neural activity and context-dependent matrix memories. Simple Markov chain simulations are presented to exemplify possible search scenarios in the contextual network. Finally, we discuss some of the characteristics of the model and the phenomenon under study.

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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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