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Delayed Synapses: An LSM Model for Studying Aspects of Temporal Context in Memory

  • Predrag Jakimovski
  • Hedda R. Schmidtke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6967)

Abstract

Spiking neural networks are promising candidates for representing aspects of cognitive context in human memory. We extended the liquid state machine model with time-delayed connections from liquid neurons to the readout unit to better capture context phenomena. We performed experiments in the area of spoken language recognition for studying two aspects of context dependency: influence of memory and temporal context. For the experiments, we derived a test data set from the well-known Brody-Hopfield test set to which we added varying degrees of Gaussian noise. We studied the influence of temporal context with a further specially designed test set. We found that the temporal context encoded in the pattern to be recognized was recognized better with our delayed synapses than without. Our experiments shed light on how context serves to integrate information and to increase robustness in human signal processing.

Keywords

Spiking neural networks (SNN) liquid state machine (LSM) context-dependency memory acquirements fading memory (FM) 

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References

  1. 1.
    Auer, P., Burgsteiner, H.M., Maass, W.: The p-delta learning rule for parallel perceptrons. Science (2002)Google Scholar
  2. 2.
    Boyd, S., Chua, L.O.: Fading memory and the problem of approximating nonlinear operators with voltera series. IEEE Trans. on Circuits and Systems 32, 1150–1161 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Feldbusch, F., Kaiser, F.: Simulation of spiking neural nets with INspiRE. In: IEEE Conference on Systems, Man and Cybernetics, SMC (2005)Google Scholar
  4. 4.
    Gerstner, W., Kistler, W.: Spiking Neuron Models. Cambridge University Press, Cambridge (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Hopfield, J.J., Brody, C.D.: What is a moment? cortical sensory integration over a brief interval. Proc. Natl. Acad. Sci. USA 97(25), 13919–13924 (2000)CrossRefGoogle Scholar
  6. 6.
    Hopfield, J.J., Brody, C.D.: What is a moment? transient synchrony as a collective mechanism for spatiotemporal integration. Proc. Nat. Acad. Sci. USA 98(3), 1282–1287 (2001)CrossRefGoogle Scholar
  7. 7.
    Jaeger, H.: Adaptive nonlinear system identification with echo state networks. Advances in Neural Information Processing Systems 15, 593–600 (2003)Google Scholar
  8. 8.
    Jaeger, H., Haas, H.: Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science 304, 78–80 (2004)CrossRefGoogle Scholar
  9. 9.
    Maass, W., Joshi, P., Sontag, E.: Computational aspects of feedback in neural circuits. PLOS Computational Biology (2006)Google Scholar
  10. 10.
    Maass, W.: Liquid state machines: Motivation, theory and applications. World Scientific Review 189 (2010)Google Scholar
  11. 11.
    Maass, W., Markram, H.: On the computational power of recurrent circuits of spiking neurons. In: Electronic Colloquium on Computational Complexity, vol. 22 (2001)Google Scholar
  12. 12.
    Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Schmidtke, H.R.: Granularity as a parameter of context. In: Dey, A.K., Kokinov, B., Leake, D.B., Turner, R. (eds.) CONTEXT 2005. LNCS (LNAI), vol. 3554, pp. 450–463. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Predrag Jakimovski
    • 1
  • Hedda R. Schmidtke
    • 1
  1. 1.Pervasive Computing Systems, TecOKarlsruheGermany

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