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Proceedings Paper

Bifurcating neuron: characterization and dynamics
Author(s): Nabil H. Farhat; Mostafa H. Eldefrawy
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Paper Abstract

We discuss the concept of bifurcating neuron and show it combines functional complexity, comparable to that of the living (biological) neuron, with structural simplicity and power efficiency which are important attributes for its hardware realizations, both optoelectronic or electronic. The functional complexity of the bifurcating neuron is key to removing many limitations of present-day neural networks which employ predominantly sigmoidal neurons that can not account for the temporal relations of firing instances of neurons in a network. In the language of information processing, accounting for such temporal relations is synonymous with retention of phase information.

The complex behavior of the bifurcating neuron is characterized. It is shown to be capable of exhibiting phase-locking and synchronization, and that it exhibits a host of firing modalities that parallel those observed by neurophysiologists in the living neuron including chaotic firing and that it is capable of bifurcating between these firing modalities depending on the nature of its input. The implications of this complex behavior for the introduction of a new generation of bifurcating neural networks, that are capable of using chaos as adaptive intrinsic noise, for self-annealing and directed intelligent search of the phase-space of bifurcating networks are discussed. It is argued that the bifurcating neuron concept is key to building new physical structures (bifurcating networks) in which one can study the roles of bifurcation, synchronicity, and chaos in collective nonlinear dynamical signal processing and is moreover key to modeling and understanding higher-level cortical signal processing such as feature-binding and cognition, and that its ease of implementation in analog hardware promises to offer important technological benefits.

Paper Details

Date Published: 2 February 1993
PDF
Proc. SPIE 1773, Photonics for Computers, Neural Networks, and Memories, (2 February 1993); doi: 10.1117/12.983185
Show Author Affiliations
Nabil H. Farhat, Univ. of Pennsylvania (United States)
Mostafa H. Eldefrawy, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 1773:
Photonics for Computers, Neural Networks, and Memories
Stephen T. Kowel; John A. Neff; William J. Miceli, Editor(s)

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