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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)

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.

Keywords

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

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References

  1. 1.
    Taketani, M., Baudry, M.: Advances in Network Electrophysiology Using Multi-Electrode Arrays. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Bakkum, D.J., Chao, Z.C., Potter, S.M.: Spatio-Temporal Electrical Stimuli Shape Behavior of an Embodied Cortical Network in a Goal-Directed Learning Task. J. Neur. Eng., 310–323 (2008)Google Scholar
  3. 3.
    Park, J., Harley, R., Venayagamoorthy, G.: Adaptive Critic Based Optimal Neurocontrol for Synchronous Generator in Power System Using MLP/RBF Neural Networks. IEEE Trans. on Industry Applications 39(5) (2003)Google Scholar
  4. 4.
    Potter, S.M., Wagenaar, D.A., DeMarse, T.B.: Closing the Loop: Stimulation Feedback Systems for Embodied MEA Cultures (2001)Google Scholar
  5. 5.
    Wagenaar, D.A.: Persistent Dynamic Attractors in Activity Patterns of Cultured Neuronal Networks. Phys. Rev. E. 73(5) (2006)Google Scholar
  6. 6.
    Potter, S.M., DeMarse, T.B.: A New Approach to Neural Cell Culture for Long-Term Studies. J. Neurosci. Methods 110, 17–24 (2001)CrossRefGoogle Scholar
  7. 7.
    Rolston, J.: Creating NeuroRighter (2009), http://groups.google.com/group/neurorighter-users
  8. 8.
    Rolston, J.D., Gross, R.E., Potter, S.M.: A Low-Cost Multielectrode System for Data Acquisition Enabling Real-Time Closed-Loop Processing with Rapid Recovery from Stimulation Artifacts. Frontiers in Neuroengineering 2(12), 1–17 (2009)Google Scholar
  9. 9.
    Maass, W., Natschlaeger, T., Markram, H.: A model for real-time computation in Generic Neural Microcircuits. Adv. Neural Info. Proc. Sys. 15, 229–236 (2003)Google Scholar
  10. 10.
    Wagenaar, D.A., Madhavan, R., Potter, S.M.: Controlling Bursting in Cortical Cultures with Multi-Electrode Stimulation. J. Neurosci. 25(3), 680–688 (2005)CrossRefGoogle Scholar
  11. 11.
    Hafizovic, S., Heer, F., et al.: A CMOS-Based Microelectrode Array for Interaction with Neuronal Cultures. J. Neurosci. Methods, 93–106 (2007)Google Scholar
  12. 12.
    Dockendorf, K.P., Park, I.: Liquid State Machines and Cultured Cortical Networks: The Separation Property. Biosystems 95, 90–97 (2009)CrossRefGoogle Scholar
  13. 13.
    Wagenaar, D.A., Potter, S.M.: Real-Time Multi-Channel Stimulus Artifact Suppression by Local Curve Fitting. J. Neurosci. Methods 120, 113–120 (2002)CrossRefGoogle Scholar
  14. 14.
    QuianQuiroga, R., Nadasdy, Z., Ben-Shaul, Y.: Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering. Neural Comput. 16, 1661–1687 (2004)CrossRefzbMATHGoogle Scholar
  15. 15.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A Library for Support Vector Machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm (September 2010) release
  16. 16.
    Geisser, S.: Predictive Inference, New York (1993)Google Scholar
  17. 17.
    Mosteller, F.: A k-sample SlippageTest for an Extreme Population. Annals of Mathematical Statistics 19(1), 58–65 (1948)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Fraser, A.S.: Simulation of Genetic Systems by Automatic Digital Computers. Australian Journal of Bio. Sci. 10, 484–491 (1957)CrossRefGoogle Scholar

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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