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

Internet

https://scholarsarchive.byu.edu/etd/1354
https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2353&context=etd

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