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Paper Abstract and Keywords
Presentation 2008-11-07 14:30
Effect of Neural Networks and Learning Data to Neuronal Activities
Masaaki Takahashi, Kiyohisa Natsume (KIT) NC2008-59
Abstract (in Japanese) (See Japanese page) 
(in English) In order to simulate neuronal electrical activities, we must estimate the dynamics of channel conductances from physiological experimental data. However, this approach requires the formulation of differential equations that express the time course of channel conductance. On the other hand, if the dynamics are automatically estimated neuronal activities can be easily simulated. By using a neural network (NN) which can learn and reproduce the dynamics, it is possible to estimate the dynamics of channel conductances without formulating the differential equations. We estimated the dynamics of the Na+ and K+ conductances of a squid giant axon using two different fully connected RNNs and were able to reproduce various neuronal activities of the axon. The reproduced activities were an action potential, a threshold, a refractory phenomenon, a rebound action potential, and periodic action potentials with a constant stimulation. Therefore, An RNN can be a useful tool to estimate the dynamics of the channel conductance of a neuron. There are several such NNs, for example, time-delayed NN, elman type NN, and fully-connected type NN. You can obtain various learning data for the NNs. The purpose is to investigate influences on neuronal activities by NNs and learning data. It was indicated that the fully-connected type NN and learning data in which voltage is changed smoothly are effective for reproducing the neuronal activities.
Keyword (in Japanese) (See Japanese page) 
(in English) Neural network / Learning data / Dynamics of channel conductance / / / / /  
Reference Info. IEICE Tech. Rep., vol. 108, no. 281, NC2008-59, pp. 1-6, Nov. 2008.
Paper # NC2008-59 
Date of Issue 2008-10-31 (NC) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee NC  
Conference Date 2008-11-07 - 2008-11-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Saga Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NC 
Conference Code 2008-11-NC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Effect of Neural Networks and Learning Data to Neuronal Activities 
Sub Title (in English)  
Keyword(1) Neural network  
Keyword(2) Learning data  
Keyword(3) Dynamics of channel conductance  
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1st Author's Name Masaaki Takahashi  
1st Author's Affiliation Kyusyu Institute of Technology (KIT)
2nd Author's Name Kiyohisa Natsume  
2nd Author's Affiliation Kyusyu Institute of Technology (KIT)
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Date Time 2008-11-07 14:30:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2008-59 
Volume (vol) vol.108 
Number (no) no.281 
Page pp.1-6 
#Pages
Date of Issue 2008-10-31 (NC) 


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