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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 ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; 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. (More)

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Paper citation in several formats:
Asakawa, S. (2012). Attractor Neural Networks for Simulating Dyslexic Patients’ Behavior. In Proceedings of the 4th International Joint Conference on Computational Intelligence (IJCCI 2012) - NCTA; ISBN 978-989-8565-33-4; ISSN 2184-3236, SciTePress, pages 582-587. DOI: 10.5220/0004170505820587

@conference{ncta12,
author={Shin{-}ichi Asakawa.},
title={Attractor Neural Networks for Simulating Dyslexic Patients’ Behavior},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence (IJCCI 2012) - NCTA},
year={2012},
pages={582-587},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004170505820587},
isbn={978-989-8565-33-4},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 4th International Joint Conference on Computational Intelligence (IJCCI 2012) - NCTA
TI - Attractor Neural Networks for Simulating Dyslexic Patients’ Behavior
SN - 978-989-8565-33-4
IS - 2184-3236
AU - Asakawa, S.
PY - 2012
SP - 582
EP - 587
DO - 10.5220/0004170505820587
PB - SciTePress