Attractor Neural Networks for Simulating Dyslexic Patients’ Behavior

Shin-ichi Asakawa

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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Paper Citation


in Harvard Style

Asakawa S. (2012). Attractor Neural Networks for Simulating Dyslexic Patients’ Behavior . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 582-587. DOI: 10.5220/0004170505820587


in Bibtex Style

@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 - Volume 1: NCTA, (IJCCI 2012)},
year={2012},
pages={582-587},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004170505820587},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - Attractor Neural Networks for Simulating Dyslexic Patients’ Behavior
SN - 978-989-8565-33-4
AU - Asakawa S.
PY - 2012
SP - 582
EP - 587
DO - 10.5220/0004170505820587