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Model of Heterogeneous Interactions Between Complex Agents. From a Neural to a Social Network

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Book cover Biologically Inspired Cognitive Architectures (BICA) for Young Scientists (BICA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 636))

Abstract

We describe a heterogeneous neural network where neurons interact by means of various neurotransmitters using the common extracellular space. Every neuron is sensitive to a subset of neurotransmitters and, when excited, secretes its specific neurotransmitter. This feature enables establishing the selective connections between neurons according to sets of their receptors and to their outputs. We use a simplification of this formalism as a basis for modeling interactions between agents in a social network, where the two opposite types of activity are spreading. Agents have beliefs of different strength and activation thresholds of different heights (which correspond to neuronal excitation thresholds) and can be more or less sensitive to an external influence (which corresponds to weights of neuron receptors). The main properties of the agents and the principles of activity spreading are defined. The classification of agents according to their parameters is provided.

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Acknowledgments

This work was supported by the Russian Science Foundation, project no. 15-07-02488.

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Authors and Affiliations

  1. V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya street, Moscow, 117997, Russia

    Liudmila Zhilyakova

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  1. Liudmila Zhilyakova
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Correspondence to Liudmila Zhilyakova .

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Editors and Affiliations

  1. Krasnow Institute, George Mason University, Fairfax, VA, USA

    Alexei V. Samsonovich

  2. National Research Nuclear University MEPhI, Moscow, Russia

    Valentin V. Klimov

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Zhilyakova, L. (2018). Model of Heterogeneous Interactions Between Complex Agents. From a Neural to a Social Network. In: Samsonovich, A., Klimov, V. (eds) Biologically Inspired Cognitive Architectures (BICA) for Young Scientists. BICA 2017. Advances in Intelligent Systems and Computing, vol 636. Springer, Cham. https://doi.org/10.1007/978-3-319-63940-6_30

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  • DOI: https://doi.org/10.1007/978-3-319-63940-6_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63939-0

  • Online ISBN: 978-3-319-63940-6

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