Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

May 15, 1998, Vol. 10, No. 4, Pages 883-902
(doi: 10.1162/089976698300017548)
© 1998 Massachusetts Institute of Technology
RST: A Connectionist Architecture to Deal with Spatiotemporal Relationships
Article PDF (2.67 MB)
Abstract

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 (ŕeseau 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.