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Proceedings Paper

Experimental analysis of a Lotka-Volterra neural network for classification
Author(s): Christopher L. Sukhu; Joseph Stanton; Marc Aylesworth
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Paper Abstract

An experimental study of a neural network modeled by an adaptive Lotka-Volterra system follows. With totally inhibitory connections, this system can be embedded in a simple classification network. This network is able to classify and monitor its inputs in a spontaneous nonlinear fashion without prior training. We describe a framework for leveraging this behavior through an example involving breast cancer diagnosis.

Paper Details

Date Published: 18 June 2015
PDF: 6 pages
Proc. SPIE 9494, Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX, 94940N (18 June 2015); doi: 10.1117/12.2177214
Show Author Affiliations
Christopher L. Sukhu, BAE Systems (United States)
Joseph Stanton, BAE Systems (United States)
Marc Aylesworth, BAE Systems (United States)


Published in SPIE Proceedings Vol. 9494:
Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX
Misty Blowers; Dan Popa; Muthu B. J. Wijesundara, Editor(s)

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