Keywords

time-invariance, liquid state machines, pattern recognition, spiking neurons

Abstract

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.