Self-organization Through Spike-Timing Dependent Plasticity Using Localized Synfire-Chain Patterns

  • Toshio Akimitsu
  • Akira Hirose
  • Yoichi Okabe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


Many experimental results suggest that more precise spike timing is significant in neural information processing. From this point of view, we construct a self-organization model using the spatiotemporal patterns, where Spike-Timing Dependent Plasticity (STDP) tunes the conduction delays between neurons. STDP forms more smoothed map with the spatially random and dispersed patterns, whereas it causes spatially distributed clustering patterns from spatially continuous and synchronous inputs. These results suggest that STDP forms highly synchronous cell assemblies changing through external stimuli to solve a binding problem.


Inhibitory Neuron Input Neuron Excitatory Neuron Conduction Delay Raster Plot 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    LeVay, S., Wiesel, T.N., Hubel, D.: The Development of Ocular Dominance columns in normal and visually deprived monkeys. J. Comp. Neurol. 191, 1–51 (1980)CrossRefGoogle Scholar
  2. 2.
    Bi, G., Poo, M.: Activity-Induced synaptic modifications in hippocampal culture: dependence on spike timing synaptic strength and cell type. J. Neuroscience 18, 10464–10472 (1998)Google Scholar
  3. 3.
    Markram, H., Lübke, J., Frotscher, M., Sakmann, B.: Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs. Science 275, 213–215 (1997)CrossRefGoogle Scholar
  4. 4.
    Song, S., Abott, L.F.: Cortical Development and Remapping through Spike Timing-Dependent Plasticity. Neuron 32, 339–350 (2001)CrossRefGoogle Scholar
  5. 5.
    Abeles, M., Bergman, H., Margalit, E., Vaadia, E.: Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. J. Neurophysiol. 70, 1629–1638 (1993)Google Scholar
  6. 6.
    Diesmann, M., Gewaltig, M.O., Aertsen, A.: Stable Propagation of synchronous spiking in cortical neural network. Nature 402, 529–533 (1999)CrossRefGoogle Scholar
  7. 7.
    Hamaguchi, K., Aihara, K.: Quantitative information transfer through layers of spiking neurons connected by Mexican-Hat-type connectivity. Neurocomputing 58-60, 85–90 (2004)CrossRefGoogle Scholar
  8. 8.
    Aviel, Y., Horn, D., Abeles, M.: Synfire waves in small balanced networks. Neurocomputing 58-60, 123–127 (2004)Google Scholar
  9. 9.
    Guyonneau, R., VanRullen, R., Thorpe, S.J.: Neurons Tune to the Earliest Spikes Through STDP. Neural Comput. 17, 859–879 (2005)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Toshio Akimitsu
    • 1
  • Akira Hirose
    • 1
  • Yoichi Okabe
    • 2
  1. 1.Department of Electronics EngineeringThe University of TokyoTokyoJapan
  2. 2.The University of the AirChiba CityJapan

Personalised recommendations