Input separability in living liquid state machines

Authors:
Robert L. Ortman;Kumar Venayagamoorthy;Steve M. Potter
Affiliations:
Laboratory for Neuroengineering, Georgia Institute of Technology and School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA;Real-Time Power and Intelligent Systems Laboratory, Missouri University of Science and Technology, Rolla, MO;Laboratory for Neuroengineering, Georgia Institute of Technology and Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA
Venue:
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Year:
2011

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Abstract

To further understand computation in living neuronal networks (LNNs) and improve artificial neural networks (NNs), we seek to create ahybrid liquid state machine (LSM) that relies on an LNN for the reservoir.This study embarks on a crucial first step, establishing effective methods for findinglarge numbers of separable input stimulation patternsin LNNs. The separation property is essential forinformation transfer to LSMs and therefore necessary for computation in our hybrid system. In order to successfully transfer information to the reservoir, it must be encoded into stimuli that reliably evoke separable responses. Candidate spatio-temporal patterns are delivered to LNNs via microelectrode arrays (MEAs), and the separability of their corresponding responses is assessed. Support vector machine (SVM)classifiers assess separability and a genetic algorithm-based method identifiessubsets of maximally separable patterns. The tradeoff between symbol set sizeand separabilityis evaluated.