Improving Liquid State Machines Through Iterative Refinement of the Reservoir

Improving Liquid State Machines Through Iterative Refinement of the Reservoir

Liquid State Machines (LSMs) exploit the power of recurrent spiking neural networks (SNNs) without training the SNN. Instead, a reservoir, or liquid, is randomly created which acts as a filter for a readout function. We develop three methods for iteratively refining a randomly generated liquid to cr...

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Bibliographic Details
Main Author:	Norton, R David
Format:	Others
Published:	BYU ScholarsArchive 2008
Subjects:	computer liquid state machine Hebbian learning reinforcement learning neural network spiking neural network machine learning Computer Sciences
Online Access:	https://scholarsarchive.byu.edu/etd/1354 https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2353&context=etd

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