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Self-Organization Process in Large Spiking Neural Networks Leading to Formation of Working Memory Mechanism

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Advances in Computational Intelligence (IWANN 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7902))

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Abstract

The subject of this work is evolutionary process in initially chaotic and homogenous spiking neural networks leading to formation of the neuron groups with partially synchronized activity (so called polychronous groups) which are not only capable of recognizing input patterns but also can keep information about pattern presentation in form of their specific activity for a long time. This result is demonstrated for very simple neuron – coincidence detector and for standard synaptic plasticity model (STDP).

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References

  1. Zipser, D., Kehoe, B., Littlewort, G., Fuster, J.: A Spiking Network Model of Short-Term Active Memory. Journal of Neuroscience 13(8), 3406–3420 (1993)

    Google Scholar 

  2. Wang, X.-J.: Synaptic reverberation underlying mnemonic persistent activity. Trends in Neurosciences 24(8), 455–463 (2001)

    Article  MATH  Google Scholar 

  3. Itskov, V., Hansel, D., Tsodyks, M.: Short-term facilitation stabilize parametric working memory trace. Frontiers in Computational Neuroscience 5, Article 40 (2011)

    Google Scholar 

  4. Mongillo, G., Barak, O., Tsodyks, M.: Synaptic Theory of Working Memory. Science 319, 1543–1546 (2008)

    Article  Google Scholar 

  5. Kiselev, M.: Self-organized Short-Term Memory Mechanism in Spiking Neural Network. In: Dobnikar, A., Lotrič, U., Šter, B. (eds.) ICANNGA 2011, Part I. LNCS, vol. 6593, pp. 120–129. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  6. Szatmary, B., Izhikevich, E.: Spike-Timing Theory of Working Memory. PLoS Comput Biol. 6(8), e1000879 (2010)

    Google Scholar 

  7. Gerstner, W., Kistler, W.: Spiking Neuron Models. Single Neurons, Populations, Plasticity. Cambridge University Press (2002)

    Google Scholar 

  8. Kiselev, M.: Self-organized Spiking Neural Network Recognizing Phase/Frequency Correlations. In: Proceedings of IJCNN 2009, Atlanta, Georgia, pp. 1633–1639 (2009)

    Google Scholar 

  9. Nolte, J.: The Human Brain: an introduction to its functional anatomy. Mosby Elsevier (2009)

    Google Scholar 

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Kiselev, M. (2013). Self-Organization Process in Large Spiking Neural Networks Leading to Formation of Working Memory Mechanism. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_51

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  • DOI: https://doi.org/10.1007/978-3-642-38679-4_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38678-7

  • Online ISBN: 978-3-642-38679-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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