Artificial Life and Robotics

, 14:371 | Cite as

Self-organization of orientation-selective and ocular-dominance maps through spike-timing-dependent plasticity

Original Article

Abstract

In the primary visual cortex, there are orientation-selective and ocular-dominance maps. These maps correlate with each other. Although many models have been proposed which explain the formation of the orientation-selective map and the ocular-dominance map, these models contain a physiologically implausible process. It is indicated that spike-timing-dependent plasticity (STDP) can yield a “topographic map” without any constraints. We show that large STDP time constants yield the orientation-selective map, and small STDP time constants yield the ocular-dominance map. This result suggests that the relationship between the orientation-selective and the ocular-dominance maps can be explained by the modulation of STDP time constants.

Key words

Orientation-selective map Ocular-dominance map Spike-timing-dependent plasticity 

References

  1. 1.
    Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160:106–154Google Scholar
  2. 2.
    Hubener M, Shoham D, Grinvald A, et al (1997) Spatial relationships among three columnar systems in cat area 17. J Neurosci 17(23):9270–9284Google Scholar
  3. 3.
    Weliky M, Katz LC (1999) Correlational structure of spontaneous neuronal activity in the developing lateral geniculate nucleous in vivo. Science 285:599–604CrossRefGoogle Scholar
  4. 4.
    Malsburg C von der (1973) Self-organization of orientation-selective cells in the striate cortex. Kybernetik 14:85–100CrossRefGoogle Scholar
  5. 5.
    Linsker R (1986) From basic network principles to neural architecture: emergence of orientation columns. Proc Natl Acad Sci USA 83:8779–8783CrossRefGoogle Scholar
  6. 6.
    Erwin E, Miller KD (1998) Correlation-based development of ocularly matched orientation and ocular dominance maps: determination of required input activities. J Neurosci 18(23):9870–9895Google Scholar
  7. 7.
    Bi G, Poo M (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18(24):10464–10472Google Scholar
  8. 8.
    Song S, Abbott LF (2001) Cortical development and remapping through spike-timing-dependent plasticity. Neuron 32:339–350CrossRefGoogle Scholar
  9. 9.
    Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Networks 14(6):1569–1572CrossRefMathSciNetGoogle Scholar

Copyright information

© International Symposium on Artificial Life and Robotics (ISAROB). 2009

Authors and Affiliations

  • Koji Iwayama
    • 1
  • Kazuyuki Aihara
    • 1
    • 2
    • 3
  • Hideyuki Suzuki
    • 1
    • 2
  1. 1.Graduate School of Information Science and TechnologyUniversity of TokyoTokyoJapan
  2. 2.Institute of Industrial ScienceUniversity of TokyoTokyoJapan
  3. 3.Aihara Complexity Modelling ProjectERATO, JSTTokyoJapan

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