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Modeling Compositionality by Dynamic Binding of Synfire Chains

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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.

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Authors and Affiliations

  1. Department of Physiology and the Center for Neural Computation, The Hebrew University, Jerusalem, Israel

    Moshe Abeles

  2. Gonda Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel

    Moshe Abeles

  3. The Center for Neural Computation, The Hebrew University, Jerusalem, Israel

    Gaby Hayon

  4. The Hebrew University, Jerusalem, Israel

    Daniel Lehmann

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  1. Moshe Abeles
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  2. Gaby Hayon
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  3. Daniel Lehmann
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Abeles, M., Hayon, G. & Lehmann, D. Modeling Compositionality by Dynamic Binding of Synfire Chains. J Comput Neurosci 17, 179–201 (2004). https://doi.org/10.1023/B:JCNS.0000037682.18051.5f

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