RBF networks for source localization in quantitative electrophysiology

Abeyratne, Udantha R., Tun, Ang Kian, Lye, Nah Teck, Guanglan, Zhang and Saratchandran, P. (2000) RBF networks for source localization in quantitative electrophysiology. Critical Reviews in Biomedical Engineering, 28 3-4: 463-472. doi:10.1615/CritRevBiomedEng.v28.i34.190

Author Abeyratne, Udantha R.
Tun, Ang Kian
Lye, Nah Teck
Guanglan, Zhang
Saratchandran, P.
Title RBF networks for source localization in quantitative electrophysiology
Journal name Critical Reviews in Biomedical Engineering   Check publisher's open access policy
ISSN 0278-940X
Publication date 2000-01
Sub-type Article (original research)
DOI 10.1615/CritRevBiomedEng.v28.i34.190
Volume 28
Issue 3-4
Start page 463
End page 472
Total pages 10
Place of publication Redding, CT United States
Publisher Begell House
Collection year 2000
Language eng
Formatted abstract
The backpropagation neural network methods have been proposed recently to solve the inverse problem in quantitative electrophysiology. A major advantage of the technique is that once a neural network is trained, it no longer requires iterations or access to sophisticated computations. We propose to use RBF networks for source localization in the brain, and systematically compare their performance to those of Levenberg-Marquardt (LM) algorithms. We show the use of two types of Radial Basis Function Networks (RBF) network: a classic network with fixed number of hidden layer neurons and an improved network. Minimal Resource Allocation Network (MRAN), recently proposed by one of the authors, capable for dynamically configuring its structure so as to obtain a compact topology to match the data presented to it.
Keyword Source localization
Minimal resource allocation
Radial basis function
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 4 times in Thomson Reuters Web of Science Article | Citations
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Created: Wed, 14 Nov 2012, 13:01:46 EST by Dr Udantha Abeyratne on behalf of School of Information Technol and Elec Engineering