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Synchrony State Generation in Artificial Neural Networks with Stochastic Synapses

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Artificial Neural Networks – ICANN 2009 (ICANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5768))

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Abstract

In this study, the generation of temporal synchrony within an artificial neural network is examined considering a stochastic synaptic model. A network is introduced and driven by Poisson distributed trains of spikes along with white-Gaussian noise that is added to the internal synaptic activity representing the background activity (neuronal noise). A Hebbian-based learning rule for the update of synaptic parameters is introduced. Only arbitrarily selected synapses are allowed to learn, i.e. change parameter values. The average of the cross-correlation coefficients between a smoothed version of the responses of all the neurons is taken as an indicator for synchrony. Results show that a network using such a framework is able to achieve different states of synchrony via learning. Thus, the plausibility of using stochastic-based models in modeling the neural process is supported. It is also consistent with arguments claiming that synchrony is a part of the memory-recall process and copes with the accepted framework in biological neural systems.

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References

  1. Tsodyks, M., Uziel, A., Markram, H.: Synchrony generation in recurrent networks with frequency-dependent synapses. J. Neurosci. 20, 50 (2000)

    Google Scholar 

  2. König, P., Engel, A.K., Roelfsema, P.R., Singer, W.: How precise is neuronal synchronization? Neural Comput. 7(3), 469–485 (1995)

    Article  Google Scholar 

  3. Hansel, D., Mato, G., Meunier, C.: Synchrony in excitatory neural networks. Neural Comput. 7(2), 307–337 (1995)

    Article  Google Scholar 

  4. Sejnowski, T.J., Paulsen, O.: Network Oscillations: Emerging Computational Principles. J. Neurosci. 26(6), 1673–1676 (2006)

    Article  Google Scholar 

  5. Fellous, J.M., Rudolph, A.M., Destexhe, B.A., Sejnowski, T.J.: Synaptic background noise controls the input/output characteristics of single cells in an in vitro model of in vivo activity. Neuroscience 122, 811–829 (2003)

    Article  Google Scholar 

  6. Lindner, B., Schimansky-Geier, L., Longtin, A.: Maximizing spike train coherence or incoherence in the leaky integrate-and-fire model. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 66(3 pt. 1), 031916 (2002)

    Article  MathSciNet  Google Scholar 

  7. Kreuz, T., Haas, J., Morelli, A., Abarbanel, H.D., Politi, A.: Measuring spike train synchrony and reliability. BMC Neuroscience 8(suppl. 2), 79 (2007)

    Article  Google Scholar 

  8. Durstewitz, D.: Self-Organizing Neural Integrator Predicts Interval Times through Climbing Activity. J. Neurosci. 4(12) (2003)

    Google Scholar 

  9. Singer, W.: Neuronal synchrony: a versatile code for the definition of relations. Neuron 24, 49–65 (1999)

    Article  Google Scholar 

  10. Gray, C.M.: The temporal correlation hypothesis of visual feature integration: Still alive and well. Neuron 24(1), 31–47 (1999)

    Article  Google Scholar 

  11. Salinas, E., Sejnowski, T.J.: Correlated neuronal activity and the flow of neural information. Nature Reviews Neuroscience 2, 539–550 (2001)

    Article  Google Scholar 

  12. von der Malsburg, C.: The what and why of binding: The modeler’s perspective (1999)

    Google Scholar 

  13. Singer, W., Gray, C.M.: Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci. 18, 555–586 (1995)

    Article  Google Scholar 

  14. Averbeck, B.B., Latham, P.E., Pouget, A.: Neural correlations, population coding and computation. Nature Reviews Neuroscience 7, 358–366 (2006)

    Article  Google Scholar 

  15. Campbell, S.R., Wang, D.L., Jayaprakash, C.: Synchrony and desynchrony in integrate-and-fire oscillators. Neural Computation 11(7), 1595–1619 (1999)

    Article  Google Scholar 

  16. Neltner, L., Hansel, D.: On synchrony of weakly coupled neurons at low firing rate. Neural Comput. 13(4), 765–774 (2001)

    Article  MATH  Google Scholar 

  17. Bressloff, P.C., Coombes, P.S.: Dynamics of strongly coupled spiking neurons. Neural Comput. 12(1), 91–129 (2000)

    Article  Google Scholar 

  18. Vreeswijk, C.V., Hansel, D.: Patterns of synchrony in neural networks with spike adaptation. Neural Comput. 13(5), 959–992 (2001)

    Article  MATH  Google Scholar 

  19. Mikula, S., Niebur, E.: Rate and synchrony in feedforward networks of coincidence detectors: Analytical solution. Neural Comput. 17(4), 881–902 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  20. DeVille, R.E.L., Peskin, C.S.: Synchrony and Asynchrony in a Fully Stochastic Neural Network. Bulletin of Mathematical Biology 70(6), 1608–1633 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  21. Holcman, D., Tsodyks, M.: The Emergence of Up and Down States in Cortical Networks. Science 2(3), 174–181 (2006)

    Google Scholar 

  22. Maass, W., Zador, A.M.: Dynamic stochastic synapses as computational units. Neural Computation 11, 903–917 (1999)

    Article  Google Scholar 

  23. Natschlager, T.: Efficient Computation in Networks of Spiking Neurons Simulations and Theory. PhD thesis, Institute of Theoretical Computer Science, Austria (1999)

    Google Scholar 

  24. Natschlager, T., Maass, W., Zador, A.: Efficient temporal processing with biologically realistic dynamic synapses. Computation in neural system 12, 75–78 (2001)

    Article  MATH  Google Scholar 

  25. Kroger, H.: Why are probabilistic laws governing quantum mechanics and neurobiology? Solitons and Fractals 25, 815 (2005)

    Article  MATH  Google Scholar 

  26. Namarvar, H.H., Liaw, J.S., Berger, T.W.: A new dynamic synapse neural network for speech recognition. IEEE Trans., 2985–2990 (2001)

    Google Scholar 

  27. Liaw, J.S., Berger, T.W.: Dynamic synapse: A new concept of neural representation and computation. Hippocampus 6, 591–600 (1996)

    Article  Google Scholar 

  28. Herzog, M.H., Esfeld, M., Gerstner, W.: Consciousness & the small network argument. Neural Networks 20(9), 1054 (2007); Brain and Consciousness

    Article  Google Scholar 

  29. Dodla, R., Wilson, C.: Synchrony-asynchrony transitions in neuronal networks. BMC Neuroscience 9(suppl. 1), 9 (2008)

    Article  Google Scholar 

  30. Schreiber, S., Fellous, J.M., Whitmer, D., Tiesinga, P., Sejnowski, T.J.: A new correlation-based measure of spike timing reliability. Neurocomputing (52-54), 925–931 (2003)

    Google Scholar 

  31. Liaw, J.S., Berger, T.W.: Computing with dynamic synapses: A case study of speech recognition. In: Proc. IEEE Int. Conf. Neural Networks, pp. 352–355 (1997)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Computer Engineering, Faculty of Mathematics and Informatics, University of Leipzig, Johannisgasse 26, 04103, Leipzig, Germany

    Karim El-Laithy & Martin Bogdan

Authors
  1. Karim El-Laithy
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  2. Martin Bogdan
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Marios Polycarpou , Christos Panayiotou  & Georgios Ellinas ,  & 

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El-Laithy, K., Bogdan, M. (2009). Synchrony State Generation in Artificial Neural Networks with Stochastic Synapses. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_19

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  • DOI: https://doi.org/10.1007/978-3-642-04274-4_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04273-7

  • Online ISBN: 978-3-642-04274-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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