Ruppin E, Reggia J A
Department of Computer Science, University of Maryland, College Park 20742.
Br J Psychiatry. 1995 Jan;166(1):19-28. doi: 10.1192/bjp.166.1.19.
Computer-supported neural network models have been subjected to diffuse, progressive deletion of synapses/neurons, to show that modelling cerebral neuropathological changes can predict the pattern of memory degradation in diffuse degenerative processes such as Alzheimer's disease. However, it has been suggested that neural models cannot account for more detailed aspects of memory impairment, such as the relative sparing of remote versus recent memories.
The latter claim is examined from a computational perspective, using a neural associative memory model.
The neural network model not only demonstrates progressive memory deterioration as diffuse network damage occurs, but also exhibits differential sparing of remote versus recent memories.
Our results show that neural models can account for a large variety of experimental phenomena characterising memory degradation in Alzheimer's patients. Specific testable predictions are generated concerning the relation between the neuraonatomical findings and the clinical manifestations of Alzheimer's disease.
计算机支持的神经网络模型已经经历了突触/神经元的弥漫性、进行性缺失,以表明对大脑神经病理变化进行建模可以预测诸如阿尔茨海默病等弥漫性退行性过程中的记忆衰退模式。然而,有人提出神经模型无法解释记忆障碍的更详细方面,例如远期记忆与近期记忆的相对保留情况。
从计算角度使用神经联想记忆模型对后一种说法进行检验。
神经网络模型不仅在发生弥漫性网络损伤时表现出进行性记忆衰退,而且还表现出远期记忆与近期记忆的差异性保留。
我们的结果表明神经模型可以解释阿尔茨海默病患者记忆衰退的多种实验现象。针对神经解剖学发现与阿尔茨海默病临床表现之间的关系产生了具体的可测试预测。