Improving Liquid State Machines Through Iterative Refinement of the Reservoir

<p>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 create a more effective one. First, we apply Hebbian learning to LSMs by building the liquid with spike-time dependant plasticity (STDP) synapses. Second, we create an eligibility based reinforcement learning algorithm for synaptic development. Third, we apply principles of Hebbian learning and reinforcement learning to create a new algorithm called separation driven synaptic modification (SDSM). These three methods are compared across four artificial pattern recognition problems, generating only fifty liquids for each problem. Each of these algorithms shows overall improvements to LSMs with SDSM demonstrating the greatest improvement. SDSM is also shown to generalize well and outperforms traditional LSMs when presented with speech data obtained from the TIMIT dataset.</p>

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Norton, R David

• computer
• liquid state machine
• Hebbian learning
• reinforcement learning
• neural network
• spiking neural network
• machine learning
• Computer Sciences

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/1354

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-2353