神经网络过程模拟支持分布式存储系统，并有助于设计一种新型的计算机自适应数字记忆测试，用于临床前和前驱期阿尔茨海默病。
Neural network process simulations support a distributed memory system and aid design of a novel computer adaptive digital memory test for preclinical and prodromal Alzheimer's disease.
机构信息
Department of Psychiatry and Psychology, Mayo Clinic, College of Medicine and Science.
Department of Neurology, Mayo Clinic, College of Medicine and Science.
出版信息
Neuropsychology. 2023 Sep;37(6):698-715. doi: 10.1037/neu0000847. Epub 2022 Aug 29.
OBJECTIVE
Growing evidence supports the importance of learning as a central deficit in preclinical/prodromal Alzheimer's disease. The aims of this study were to conduct a series of neural network simulations to develop a functional understanding of a distributed, nonmodular memory system that can learn efficiently without interference. This understanding is applied to the development of a novel digital memory test.
METHOD
Simulations using traditional feed forward neural network architectures to learn simple logic problems are presented. The simulations demonstrate three limitations: (a) inefficiency, (b) an inability to learn problems consistently, and (c) catastrophic interference when given multiple problems. A new mirrored cascaded architecture is introduced to address these limitations, with support provided by a series of simulations.
RESULTS
The mirrored cascaded architecture demonstrates efficient and consistent learning relative to feed forward networks but also suffers from catastrophic interference. Addition of context values to add the capability of distinguishing features as part of learning eliminates the problem of interference in the mirrored cascaded, but not the feed forward, architectures.
CONCLUSIONS
A mirrored cascaded architecture addresses the limitations of traditional feed forward neural networks, provides support for a distributed memory system, and emphasizes the importance of context to avoid interference. These process models contributed to the design of a digital computer-adaptive word list learning test that places maximum stress on the capability to distinguish specific episodes of learning. Process simulations provide a useful method of testing models of brain function and contribute to new approaches to neuropsychological assessment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
目的
越来越多的证据支持学习能力下降是临床前/前驱期阿尔茨海默病的核心缺陷。本研究旨在进行一系列神经网络模拟，以深入了解一种分布式、非模块化的记忆系统，该系统能够在不受干扰的情况下高效学习。这一理解应用于新型数字记忆测试的开发。
方法
提出了使用传统前馈神经网络架构来学习简单逻辑问题的模拟。这些模拟表明存在三个局限性：（a）效率低下，（b）无法一致地学习问题，以及（c）在给定多个问题时会出现灾难性干扰。引入了一种新的镜像级联架构来解决这些局限性，并用一系列模拟来提供支持。
结果
镜像级联架构相对于前馈网络具有高效和一致的学习能力，但也存在灾难性干扰。添加上下文值以添加区分特征的能力作为学习的一部分，可以消除镜像级联架构中的干扰问题，但不能消除前馈架构中的干扰问题。
结论
镜像级联架构解决了传统前馈神经网络的局限性，为分布式记忆系统提供了支持，并强调了上下文对于避免干扰的重要性。这些过程模型有助于设计一种数字计算机自适应单词列表学习测试，该测试对区分学习特定情节的能力施加了最大压力。过程模拟为大脑功能模型的测试提供了一种有用的方法，并为神经心理学评估的新方法做出了贡献。（PsycInfo 数据库记录（c）2023 APA，保留所有权利）。
Zotero 插件