Comparing artificial and biological dynamical neural networks

Alastair D. McAulay

doi:10.1117/12.666659

17 May 2006 Comparing artificial and biological dynamical neural networks

Alastair D. McAulay

Proceedings Volume 6235, Signal Processing, Sensor Fusion, and Target Recognition XV; 62351G (2006) https://doi.org/10.1117/12.666659
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

Alastair D. McAulay "Comparing artificial and biological dynamical neural networks", Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 62351G (17 May 2006); https://doi.org/10.1117/12.666659

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