Share Email Print

Proceedings Paper

Pulse-coupled neural networks
Author(s): John L. Johnson
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

A pulse-coupled neural network is shown to contain invariant spatial information in the phase structure of the output pulse trains. Two time scales are identified. On the fast time scale the linking produces dynamic, periodic, fringe-like traveling waves. The slow time scale is set by the pulse generator, and on that scale the image is segmented into multi-neuron time-synchronous groups. These groups, by the same linking mechanism, can form periodic pulse structures whose relative phases encode the location of the groups with respect to one another. The time signals are a unique, object-specific and roughly invariant time signature for their corresponding input spatial image or distribution. The details of the model are discussed, giving the basic linking field model, extensions, generation of time series in the limit of very weak linking, invariances from the symmetries of the receptive fields, time scales, waves, and signatures. Multi-rule logical systems are shown to exist on single neurons. Adaptation is discussed. Hardware implementations, optical and electronic, are reviewed. The conjugate basic problem of transforming a time signal into a spatial distribution is discussed.

Paper Details

Date Published: 1 March 1994
PDF: 30 pages
Proc. SPIE 10277, Adaptive Computing: Mathematics, Electronics, and Optics: A Critical Review, 1027705 (1 March 1994); doi: 10.1117/12.171194
Show Author Affiliations
John L. Johnson, U.S. Army Missile Command (Germany)

Published in SPIE Proceedings Vol. 10277:
Adaptive Computing: Mathematics, Electronics, and Optics: A Critical Review
Su-Shing Chen; H. John Caulfield, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?