Journal of Computational Neuroscience

, Volume 17, Issue 2, pp 179–201 | Cite as

Modeling Compositionality by Dynamic Binding of Synfire Chains

  • Moshe Abeles
  • Gaby Hayon
  • Daniel Lehmann

Abstract

This paper examines the feasibility of manifesting compositionality by a system of synfire chains. Compositionality is the ability to construct mental representations, hierarchically, in terms of parts and their relations. We show that synfire chains may synchronize their waves when a few orderly cross links are available. We propose that synchronization among synfire chains can be used for binding component into a whole. Such synchronization is shown both for detailed simulations, and by numerical analysis of the propagation of a wave along a synfire chain. We show that global inhibition may prevent spurious synchronization among synfire chains. We further show that selecting which synfire chains may synchronize to which others may be improved by including inhibitory neurons in the synfire pools. Finally we show that in a hierarchical system of synfire chains, a part-binding problem may be resolved, and that such a system readily demonstrates the property of priming. We compare the properties of our system with the general requirements for neural networks that demonstrate compositionality.

synfire-chains compositionality binding-mechanism neural-networks 

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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Moshe Abeles
    • 1
    • 2
  • Gaby Hayon
    • 3
  • Daniel Lehmann
    • 4
  1. 1.Department of Physiology and the Center for Neural ComputationThe Hebrew UniversityJerusalemIsrael
  2. 2.Gonda Brain Research CenterBar-Ilan UniversityRamat-GanIsrael
  3. 3.The Center for Neural ComputationThe Hebrew UniversityJerusalemIsrael
  4. 4.The Hebrew UniversityJerusalemIsrael

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