Cognitive Neurodynamics

, Volume 3, Issue 1, pp 33–38 | Cite as

Multiple topological representation self-organized by spike-timing-dependent synaptic learning rule

Research Article
  • 75 Downloads

Abstract

Position-and-scale-free representations of shapes are acquired by neurons in the inferior temporal (IT) cortex. So each neuron receives information from the whole visual field. Familiar shapes are extremely restricted from all the possible shapes on the whole visual field. So they must be clustered in the shape space to have mixed structure of continuity and discreteness. We demonstrate that multiple representation can be acquired in a spike-based model for topological maps based on the spike-timing-dependent synaptic plasticity (STDP), subjected to a set of inputs on multiple rings, which is a simple example of mixed structure. In this representation, the position on each ring is represented by a center of active neurons and the difference of rings is represented by a detailed pattern of active neurons. Neurons in the same region exhibit high activities for an input on the other ring. The result is consistent with the fact observed in IT cortex that neighboring neurons exhibit different preferences while the region of active neurons is continuously shifted for continuous changes of object.

Keywords

Topological map Inferior temporal cortex Spike-timing-dependent plasticity 

Notes

Acknowledgment

This work is supported in part by Grants-in-Aid for Scientific Research on Priority Areas, No.18019034.

References

  1. Anderson J, Rosenfeld E (eds) (1988) Neurocomputing: foundations of research. MIT Press, CambridgeGoogle Scholar
  2. Bi G, Poo M (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18:10464–10472PubMedGoogle Scholar
  3. Fujita I, Tanaka K, Ito M, Cheng K (1992) Columns for visual features of objects in monkey inferotemporal cortex. Nature 360:343–346PubMedCrossRefGoogle Scholar
  4. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biological Cybernetics 43. Reprinted in (Anderson and Rosenfeld, 1988)Google Scholar
  5. Song S, Abbott LF (2001) Cortical development and remapping through spike timing-dependent plasticity. Neuron 32:339–350PubMedCrossRefGoogle Scholar
  6. Takeuchi A, Amari S (1979) Formation of topographic maps and columnar microstructures in nerve fields. Biol Cybern 35:63–72PubMedCrossRefGoogle Scholar
  7. Tamura H, Kaneko H, Fujita I (2005) Quantitative analysis of functional clustering of neurons in the macaque inferior temporal cortex. Neurosci Res 52:311–322PubMedCrossRefGoogle Scholar
  8. von der Malsburg C (1973) Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14. Reprinted in (Anderson and Rosenfeld, 1988)Google Scholar
  9. Wada K, Kurata K, Okada M (2004) Self-organization of globally continuous and locally distributed information representation. Neural Netw 17:1039–1049PubMedCrossRefGoogle Scholar
  10. Wang G, Tanifuji M, Tanaka K (1998) Functional architecture in monkey inferotemporal cortex revealed by in vivo optical imaging. Nerosci Res 32:33–46CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Tamagawa University Brain Science InstituteMachida-shiJapan
  2. 2.Department of Electrical EngineeringKochi National College of TechnologyKochiJapan

Personalised recommendations