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Critical Reviews™ in Biomedical Engineering
SJR: 0.26 SNIP: 0.375 CiteScore™: 1.4

ISSN Imprimir: 0278-940X
ISSN En Línea: 1943-619X

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Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.v28.i34.190
pages 463-472

RBF Networks for Source Localization in Quantitative Electrophysiology

Udantha R. Abeyratne
School of Electrical and Electronics Engineering and Biomedical Engineering Research Center, Nanyang Technological University, Nanyang Avenue, Singapore 639798
Ang Kian Tun
School of Electrical and Electronics Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798
Nah Teck Lye
School of Electrical and Electronics Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798
Zhang Guanglan
School of Electrical and Electronics Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798
P. Saratchandran
School of Electrical and Electronics Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798

SINOPSIS

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.


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