DocumentCode :
1365716
Title :
Fast numerical integration of relaxation oscillator networks based on singular limit solutions
Author :
Linsay, Paul S. ; Wang, DeLiang L.
Author_Institution :
Plasma Fusion Center, MIT, Cambridge, MA, USA
Volume :
9
Issue :
3
fYear :
1998
fDate :
5/1/1998 12:00:00 AM
Firstpage :
523
Lastpage :
532
Abstract :
Relaxation oscillations exhibiting more than one time scale arise naturally from many physical systems. When relaxation oscillators are coupled in a way that resembles chemical synapses, we propose a fast method to numerically integrate such networks. The numerical technique, called the singular limit method, is derived from analysis of relaxation oscillations in the singular limit. In such a limit, system evolution gives rise to time instants at which fast dynamics take place and intervals between them during which slow dynamics take place. A full description of the method is given for a locally excitatory globally inhibitory oscillator network (LEGION), where fast dynamics, characterized by jumping which leads to dramatic phase shifts, is captured in this method by iterative operation and slow dynamics is entirely solved. The singular limit method is evaluated by computer experiments, and it produces remarkable speedup compared to other methods of integrating these systems. The speedup makes it possible to simulate large-scale oscillator networks
Keywords :
integration; neural nets; relaxation oscillators; fast dynamics; fast numerical integration; large-scale oscillator networks; locally excitatory globally inhibitory oscillator network; phase shifts; relaxation oscillator networks; singular limit solutions; slow dynamics; system evolution; time instants; Associate members; Biological system modeling; Chemicals; Computational modeling; Evolution (biology); Image analysis; Iterative methods; Local oscillators; Neurodynamics; Neurons;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.668894
Filename :
668894
Link To Document :
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