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Proceedings Paper

Comparing artificial and biological dynamical neural networks
Author(s): Alastair D. McAulay
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Paper Abstract

Modern computers can be made more friendly and otherwise improved by making them behave more like humans. Perhaps we can learn how to do this from biology in which human brains evolved over a long period of time. Therefore, we first explain a commonly used biological neural network (BNN) model, the Wilson-Cowan neural oscillator, that has cross-coupled excitatory (positive) and inhibitory (negative) neurons. The two types of neurons are used for frequency modulation communication between neurons which provides immunity to electromagnetic interference. We then evolve, for the first time, an artificial neural network (ANN) to perform the same task. Two dynamical feed-forward artificial neural networks use cross-coupling feedback (like that in a flip-flop) to form an ANN nonlinear dynamic neural oscillator with the same equations as the Wilson-Cowan neural oscillator. Finally we show, through simulation, that the equations perform the basic neural threshold function, switching between stable zero output and a stable oscillation, that is a stable limit cycle. Optical implementation with an injected laser diode and future research are discussed.

Paper Details

Date Published: 17 May 2006
PDF: 7 pages
Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 62351G (17 May 2006); doi: 10.1117/12.666659
Show Author Affiliations
Alastair D. McAulay, Lehigh Univ. (United States)


Published in SPIE Proceedings Vol. 6235:
Signal Processing, Sensor Fusion, and Target Recognition XV
Ivan Kadar, Editor(s)

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