Neural Computation, Vol 10, 883-902, Copyright © 1998 by The MIT Press
RST: A Connectionist Architecture To Deal With Spatiotemporal Relationships
J. C. Chappelier and A. Grumbach
In the past decade, connectionism has proved its efficiency in the field of
static pattern recognition. The next challenge is to deal with
spatiotemporal problems. This article presents a new connectionist
architecture, RST (réseau spatio temporel
[spatio temporal network]), with such spatiotemporal capacities. It aims at
taking into account at the architecture level both spatial relationships
(e.g., as between neighboring pixels in an image) and temporal
relationships (e.g., as between consecutive images in a video sequence).
Concerning the spatial aspect, the network is embedded in actual space
(two- or three-dimensional), the metrics of which directly influence its
structure through a connection distribution function. For the temporal
aspect, we looked toward biology and used a leaky-integrator neuron model
with a refractory period and postsynaptic potentials. The propagation of
activity by spatiotemporal synchronized waves enables RST to perform motion
detection and localization in sequences of video images.
