Experimental analysis of a Lotka-Volterra neural network for classification

Christopher L. Sukhu; Joseph Stanton; Marc Aylesworth

doi:10.1117/12.2177214

18 June 2015 Experimental analysis of a Lotka-Volterra neural network for classification

Christopher L. Sukhu, Joseph Stanton, Marc Aylesworth

Author Affiliations +

Proceedings Volume 9494, Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX; 94940N (2015) https://doi.org/10.1117/12.2177214
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

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.

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Christopher L. Sukhu, Joseph Stanton, and Marc Aylesworth "Experimental analysis of a Lotka-Volterra neural network for classification", Proc. SPIE 9494, Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX, 94940N (18 June 2015); https://doi.org/10.1117/12.2177214

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