Input Separability in Living Liquid State Machines

  • Robert L. Ortman
  • Kumar Venayagamoorthy
  • Steve M. Potter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)


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.


Separation property cultured neuronal network liquid state machine support vector machine microelectrode array 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Robert L. Ortman
    • 1
    • 3
  • Kumar Venayagamoorthy
    • 4
  • Steve M. Potter
    • 1
    • 2
  1. 1.Laboratory for NeuroengineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Coulter Department of Biomedical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  3. 3.School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  4. 4.Real-Time Power and Intelligent Systems LaboratoryMissouri University of Science and TechnologyRollaUSA

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