Advertisement

New Approaches to Discrete Modeling of Natural Neural Networks

  • Oleg Kuznetsov
  • Ludmila Zhilyakova
  • Nikolay Bazenkov
  • Boris Boldyshev
  • Sergey Kulivets
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 874)

Abstract

A discrete model of multitransmitter interactions between neurons in a common extracellular space (ECS) is proposed. Neurons in the model are heterogeneous in three different senses. They differ in (i) the type of endogenous change in the membrane potential, (ii) the type of secreted neurotransmitter, and (iii) the set of receptors, wherein each receptor is sensitive to a particular neurotransmitter. The model is characterized by the broadcast nature transmission of the signals: the neurotransmitter appeared in the ECS is treated as an input signal for all neurons with receptors sensitive to it. It is shown that the extrasynaptic interaction of neurons combined with multitransmitter environment enables to reproduce the rhythms generated by simple natural neural networks.

Keywords

Discrete model of natural neural network Chemical interactions Neurotransmitters Endogenous activity 

References

  1. 1.
    Baronchelli, A., Ferrer-i-Cancho, R., Pastor-Satorras, R., Chater, N., Christiansen, M.H.: Networks in cognitive science. Trends Cogn. Sci. 17(7), 348–360 (2013)CrossRefGoogle Scholar
  2. 2.
    Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009)CrossRefGoogle Scholar
  3. 3.
    Bargmann, C.I.: Beyond the connectome: How neuromodulators shape neural circuits. BioEssays 34(6), 458–465 (2012)CrossRefGoogle Scholar
  4. 4.
    Dyakonova, V.E.: Neurotransmitter mechanisms of context-dependent behavior. Neurosci. Behav. Physiol. 44, 256–267 (2014)CrossRefGoogle Scholar
  5. 5.
    Dyakonova, V.: Neurotransmitternye mechanismy context-zavisimogo povedenija. Zh. Vyssh. Nerv. Deyat. 62(6), 1–17 (2012). (in Russian)Google Scholar
  6. 6.
    Sakharov, D.: Biologicheskij substrat generatsii povedencheskih actov. Zh. Obsch. Biol. 73(5), 334–348 (2012). (in Russian)Google Scholar
  7. 7.
    Bloom, F.E.: The functional significance of neurotransmitter diversity. Am. J. Physiol. 246, 184–194 (1984)CrossRefGoogle Scholar
  8. 8.
    Getting, P.: Emerging principles governing the operation of neural networks. Annu. Rev. Neurosci. 12, 185–204 (1989)CrossRefGoogle Scholar
  9. 9.
    Sakharov, D.: The multiplicity of neurotransmitters: the functional significance. Zh. Evol. Biokhim. Fiziol. 26(5), 733–741 (1990). (in Russian)Google Scholar
  10. 10.
    Brezina, V.: Beyond the wiring diagram: signalling through complex neuromodulator networks. Philos. Trans. R. Soc. Lond. B Biol. Sci. 12; 365(1551), 2363–2374 (2010)CrossRefGoogle Scholar
  11. 11.
    Moroz, L.L., Kohn, A.B.: Independent origins of neurons and synapses: insights from ctenophores. Phil. Trans. R. Soc. Lond. B. Biol. Sci. 371(1685), 20150041 (2016)CrossRefGoogle Scholar
  12. 12.
    Bazenkov, N., Vorontsov, D., Dyakonova, V., Zhilyakova, L., Zakharov, I., Kuznetsov, O., Kulivets, S., Sakharov, D.: Discrete modeling of inter-neuron interactions in multitransmitters networks. Iskusstvenny Intellekt i Prinyatie Reshenii [Artif. Intell. Decis. Mak.] 2, 55–73 (2017)Google Scholar
  13. 13.
    Bazenkov, N., Dyakonova, V., Kuznetsov, O., Sakharov, D., Vorontsov, D., Zhilyakova, L.: Discrete modeling of multi-transmitter neural networks with neuronal competition. In: Biologically Inspired Cognitive Architectures (BICA) for Young Scientists. Advances in Intelligent Systems and Computing, vol. 636, pp. 10–16. Springer International Publishing AG (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Oleg Kuznetsov
    • 1
  • Ludmila Zhilyakova
    • 1
  • Nikolay Bazenkov
    • 1
  • Boris Boldyshev
    • 1
  • Sergey Kulivets
    • 1
  1. 1.Trapeznikov Institute of Control Sciences of RASMoscowRussia

Personalised recommendations