liquid state machines, Hebbian learning, separation
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). All Faculty Publications. 305.
Physical and Mathematical Sciences
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