Author:
Shin-ichi Asakawa
Affiliation:
Tokyo Woman's Christian University, Japan
Keyword(s):
Attractor Neural Network, Reaction Time, Identification and Categorization Tasks, Orthography, Phonology and Semantics, Brain Damaged Patients.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
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Artificial Intelligence and Decision Support Systems
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Biomedical Engineering
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Biomedical Signal Processing
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Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
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Neural Network Software and Applications
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Neural Networks
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Neurocomputing
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Neurotechnology, Electronics and Informatics
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Pattern Recognition
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Physiological Computing Systems
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Sensor Networks
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Signal Processing
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Soft Computing
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Theory and Methods
Abstract:
It was investigated that the ability of an attractor neural network. The attractor neural network can be applicable to various symptoms of brain damaged patients. It can account for delays in reaction times in word reading and word identification tasks. Because the iteration numbers of mutual connections between an output and a cleanup layers might increase, when they are partially damaged. This prolongation looks or behaves the delays of reaction times of brain damaged patients. When we applied the attractor neural network to the data of Tyler et al. (2000) for categorization task, it showed a kind of category specific phenomenon. In this sense, the attractor neural network could explain an aspect of the category specific disorders. In this sense the attractor network might simulate the human semantic memory organization. In spite of variations in data, and in spite of the simplicity of the architecture, the attractor network showed good performances. We could say that the attractor
network succeeded in mimicking human normal subjects and brain damaged patients. The possibility of explaining the triangle model (Plaut & McClelland,1989; Plaut, McClelland, Seidenberg, and Patterson, 1996) also discussed.
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