Keywords
liquid state machines, Hebbian learning, separation
Abstract
In Liquid State Machines, separation is a critical attribute of the liquid—which is traditionally not trained. The effects of using Hebbian learning in the liquid to improve separation are investigated in this paper. When presented with random input, Hebbian learning does not dramatically change separation. However, Hebbian learning does improve separation when presented with real-world speech data.
Original Publication Citation
David Norton and Dan Ventura, "Preparing More Effective Liquid State Machines Using Hebbian Learning", Proceedings of the International Joint Conference on Neural Networks, pp. 8359-8364, July 26.
BYU ScholarsArchive Citation
Norton, David and Ventura, Dan A., "Preparing More Effective Liquid State Machines Using Hebbian Learning" (2006). Faculty Publications. 305.
https://scholarsarchive.byu.edu/facpub/305
Document Type
Peer-Reviewed Article
Publication Date
2006-07-01
Permanent URL
http://hdl.lib.byu.edu/1877/2531
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
http://lib.byu.edu/about/copyright/