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Controlling the speed of synfire chains

  • Oral Presentations: Neurobiology Neurobiology IV: Temporal Processing
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Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

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Abstract

This paper deals with the propagation velocity of synfire chain activation in locally connected networks of artificial spiking neurons. Analytical expressions for the propagation speed are derived taking into account form and range of local connectivity, explicitly modelled synaptic potentials, transmission delays and axonal conduction velocities. Wave velocities particularly depend on the level of external input to the network indicating that synfire chain propagation in real networks should also be controllable by appropriate inputs. The results are numerically tested for a network consisting of ‘integrate-and-fire’ neurons.

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References

  1. Abeles, M.: Corticonics: neural circuits of the cerebral cortex. Cambridge University Press, Cambridge UK, 1991

    Google Scholar 

  2. Abeles, M., Bergman, H., Gat, I., Meilijson, I., Seidemann, E., Tishby, N., and Vaadia, E.: Cortical Activity Flips Among Quasi Stationary States. PNAS 92 (1995) 8616–8620

    Google Scholar 

  3. Arndt, M., Erlhagen, W., and Aertsen, A.: Propagation of Synfire Activity in Cortical Networks — a Dynamical Systems Approach. In: Lappen, B. and Gielen, S.: Neural Networks: Artificial Intelligence and Industrial Applications. Proceedings of the Third Annual SNN Symposium on Neural Networks. Springer, Berlin, 1995

    Google Scholar 

  4. Arnoldi, H.M.R., Brauer, W.: Synchronization without oscillatory neurons. Biol.Cybern. 74 (1996) 209–223

    Google Scholar 

  5. Bienenstock, E.: A model of the neocortex. Network 6 (1995) 179–224

    Google Scholar 

  6. Idiart, M.A.P., Abbott, L.F.: Propagation of excitation in neural network models. Network 4 (1993) 285–294

    Google Scholar 

  7. Palm, G.: On the internal structure of cell assemblies. In Aertsen, A. (ed) Brain Theorie, pp 261–271. Elsevier Science Publishers, Amsterdam, 1993

    Google Scholar 

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Authors and Affiliations

  1. Department of Neural Information Processing, University of Ulm, D-89069, Ulm, Germany

    Thomas Wennekers & Günther Palm

Authors
  1. Thomas Wennekers
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  2. Günther Palm
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Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Wennekers, T., Palm, G. (1996). Controlling the speed of synfire chains. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_78

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  • DOI: https://doi.org/10.1007/3-540-61510-5_78

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

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