time-invariance, liquid state machines, pattern recognition, spiking neurons
Time invariant recognition of spatiotemporal patterns is a common task of signal processing. Liquid state machines (LSMs) are a paradigm which robustly handle this type of classification. Using an artificial dataset with target pattern lengths ranging from 0.1 to 1.0 seconds, we train an LSM to find the start of the pattern with a mean absolute error of 0.18 seconds. Also, LSMs can be trained to identify spoken digits, 1-9, with an accuracy of 97.6%, even with scaling by factors ranging from 0.5 to 1.5.
Original Publication Citation
Eric Goodman and Dan Ventura, "Time Invariance and Liquid State Machines", Proceedings of the Joint Conference on Information Sciences, pp. 42-423, July 25.
BYU ScholarsArchive Citation
Goodman, Eric and Ventura, Dan A., "Time Invariance and Liquid State Machines" (2005). All Faculty Publications. 366.
Physical and Mathematical Sciences
© 2005 Dan Ventura et al.
Copyright Use Information