Expanding the structure of shunting inhibitory artificial neural network classifiers

Ganesh Arulampalam; Abdesselam Bouzerdoum

Expanding the structure of shunting inhibitory artificial neural network classifiers

Ganesh Arulampalam^*, Abdesselam Bouzerdoum

^*Corresponding author for this work

Edith Cowan University

Research output: Contribution to conference › Paper › peer-review

3 Citations (Scopus)

Abstract

Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks in which the neurons interact via a nonlinear mechanism called shunting inhibition. They are capable of producing complex, nonlinear decision boundaries. The structure and operation of feedforward SIANNs and some enhancements are presented. They are applied to several classification problems, and their performance is compared to that of the multilayer perceptron classifier.

Original language	English
Pages	2855-2860
Number of pages	6
Publication status	Published - 2002
Externally published	Yes
Event	2002 International Joint Conference on Neural Networks (IJCNN'02) - Honolulu, HI, United States Duration: 12 May 2002 → 17 May 2002

Conference

Conference	2002 International Joint Conference on Neural Networks (IJCNN'02)
Country/Territory	United States
City	Honolulu, HI
Period	12/05/02 → 17/05/02

Cite this

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abstract = "Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks in which the neurons interact via a nonlinear mechanism called shunting inhibition. They are capable of producing complex, nonlinear decision boundaries. The structure and operation of feedforward SIANNs and some enhancements are presented. They are applied to several classification problems, and their performance is compared to that of the multilayer perceptron classifier.",

author = "Ganesh Arulampalam and Abdesselam Bouzerdoum",

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AU - Arulampalam, Ganesh

AU - Bouzerdoum, Abdesselam

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AB - Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks in which the neurons interact via a nonlinear mechanism called shunting inhibition. They are capable of producing complex, nonlinear decision boundaries. The structure and operation of feedforward SIANNs and some enhancements are presented. They are applied to several classification problems, and their performance is compared to that of the multilayer perceptron classifier.

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M3 - Paper

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T2 - 2002 International Joint Conference on Neural Networks (IJCNN'02)

Y2 - 12 May 2002 through 17 May 2002

ER -

Expanding the structure of shunting inhibitory artificial neural network classifiers

Abstract

Conference

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