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The European Physical Journal Special Topics

, Volume 227, Issue 10–11, pp 1039–1049 | Cite as

Multilayer adaptive networks in neuronal processing

  • Adrián Hernández
  • José M. AmigóEmail author
Regular Article
Part of the following topical collections:
  1. Advances in Nonlinear Dynamics of Complex Networks: Adaptivity, Stochasticity, Delays

Abstract

The connectome is a wiring diagram mapping all the neural connections in the brain. At the cellular level, it provides a map of the neurons and synapses within a part or all of the brain of an organism. In recent years, significant advances have been made in the study of the connectome via network science and graph theory. This analysis is fundamental to understand neurotransmission (fast synaptic transmission) networks. However, neurons use other forms of communication as neuromodulation that, instead of conveying excitation or inhibition, change neuronal and synaptic properties. This additional neuromodulatory layers condition and reconfigure the connectome. In this paper, we propose that multilayer adaptive networks, in which different synaptic and neurochemical layers interact, are the appropriate framework to explain neuronal processing. Then, we describe a simplified multilayer adaptive network model that accounts for these extra-layers of interaction and analyse the emergence of interesting computational capabilities.

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References

  1. 1.
    O. Sporns, G. Tononi, R. Kötter, PLoS Computat. Biol. 1, e42 (2005) ADSCrossRefGoogle Scholar
  2. 2.
    A.M. Zador, J. Dubnau, H.K. Oyibo, H. Zhan, G. Cao, I.D. Peikon, PLoS Biol. 10, e1001411 (2012) CrossRefGoogle Scholar
  3. 3.
    A. Horn, D. Ostwald, M. Reisert, F. Blankenburg, NeuroImage 102, 142 (2014) CrossRefGoogle Scholar
  4. 4.
    D.J. Felleman, D.C. Van Essen, Cereb. Cortex 1, 1 (1991) CrossRefGoogle Scholar
  5. 5.
    W. Gerstner, W. Kistler, R. Naud, L. Paninski, Neuronal Dynamics (Cambridge University Press, Cambridge, UK, 2014) Google Scholar
  6. 6.
    C. Bargmann, E. Marder, Nat. Methods 10, 483 (2013) CrossRefGoogle Scholar
  7. 7.
    C. Bargmann, BioEssays 34, 458 (2012) CrossRefGoogle Scholar
  8. 8.
    V. Brezina, Philos. Trans. R. Soc. B 365, 2363 (2010) CrossRefGoogle Scholar
  9. 9.
    E. Marder, Neuron 76, 1 (2012) CrossRefGoogle Scholar
  10. 10.
    D. Bucher, E. Marder, Cell 155, 482 (2013) CrossRefGoogle Scholar
  11. 11.
    J.G. White, E. Southgate, J.N. Thomson, S. Brenner, Philos. Trans. R. Soc. Lond. B 314, 1 (1986) ADSCrossRefGoogle Scholar
  12. 12.
    L.R. Varshney, B.L. Chen, E. Paniagua, D.H. Hall, D.B. Chklovskii, PLoS Computat. Biol. 7, e1001066 (2011) ADSCrossRefGoogle Scholar
  13. 13.
    S. Achard, E. Bullmore, PLoS Computat. Biol. 3, e17 (2007) ADSCrossRefGoogle Scholar
  14. 14.
    A.P. Alivisatos, M. Chun, G.M. Church, R.J. Greenspan, M.L. Roukes, R. Yuste, Neuron 74, 970 (2012) CrossRefGoogle Scholar
  15. 15.
    A.F. Alexander-Bloch, P.E. Vértes, R. Stidd, F. Lalonde, L. Clasen, J. Rapoport, J. Giedd, E.T. Bullmore, N. Gogtay, Cereb. Cortex 23, 127 (2013) CrossRefGoogle Scholar
  16. 16.
    S.T. Baker, D.I. Lubman, M. Yücel, N.B. Allen, S. Whittle, B.D. Fulcher, A. Zalesky, A. Fornito, J. Neurosci. 35, 9078 (2015) CrossRefGoogle Scholar
  17. 17.
    L. Barnett, C.L. Buckley, S. Bullock, Phys. Rev. E 79, 051914 (2009) ADSMathSciNetCrossRefGoogle Scholar
  18. 18.
    V. Nicosia, P.E. Vértes, W.R. Schafer, V. Latora, E.T. Bullmore, PNAS 110, 7880 (2013) ADSCrossRefGoogle Scholar
  19. 19.
    E.K. Towlson, P.E. Vértes, S.E. Ahnert, W.R. Schafer, E.T. Bullmore, J. Neurosci. 33, 6380 (2013) CrossRefGoogle Scholar
  20. 20.
    F. Nadim, D. Bucher, Curr. Opin. Neurobiol. 29, 48 (2014) CrossRefGoogle Scholar
  21. 21.
    F. Fröhlich, Network Neuroscience (Academic Press, London, 2016) Google Scholar
  22. 22.
    A. Fornito, A. Zalesky, E. Bullmore, Fundamentals of Brain Network Analysis (Academic Press, London, 2016) Google Scholar
  23. 23.
    H. Sayama, I. Pestov, J. Schmidt, B.J. Bush, C. Wong, J. Yamanoi, T. Gross, Comput. Math. Appl. 65, 1645 (2013) MathSciNetCrossRefGoogle Scholar
  24. 24.
    O.V. Maslennikov, V.I. Nekorkin, Physics-Uspekhi 60, 694 (2017) ADSCrossRefGoogle Scholar
  25. 25.
    M. Wiedermann, J.F. Donges, J. Heitzig, W. Lucht, J. Kurths, Phys. Rev. E 91, 052801 (2015) ADSMathSciNetCrossRefGoogle Scholar
  26. 26.
    T. Aoki, L.E.C. Rocha, T. Gross, Phys. Rev. E 93, 040301 (2016) ADSCrossRefGoogle Scholar
  27. 27.
    T. Aoki, K. Yawata, T. Aoyagi, Phys. Rev. E 91, 012908 (2015) ADSMathSciNetCrossRefGoogle Scholar
  28. 28.
    M. Kivelä, A. Arenas, M. Barthelemy, J.P. Gleeson, Y. Moreno, M.A. Porter, J. Complex Netw. 2, 203 (2014) CrossRefGoogle Scholar
  29. 29.
    M. De Domenico, C. Granell, M.A. Porter, A. Arenas, Nat. Phys. 12, 901 (2016) CrossRefGoogle Scholar
  30. 30.
    M. De Domenico, Gigascience 6, 1 (2017) CrossRefGoogle Scholar
  31. 31.
    G. Menichetti, D. Remondini, P. Panzarasa, R.J. Mondragón, G. Bianconi, PLoS One 9, e97857 (2014) ADSCrossRefGoogle Scholar
  32. 32.
    N. Kopell, H.J. Gritton, M.A. Whittington, M.A. Kramer, Neuron 83, 1319 (2014) CrossRefGoogle Scholar
  33. 33.
    N. Daur, F. Nadim, D. Bucher, Curr. Opin. Neurobiol. 41, 1 (2016) CrossRefGoogle Scholar
  34. 34.
    B. Bentley, R. Branicky, C.L. Barnes, Y.L. Chew, E. Yemini, E.T. Bullmore, P.E. Vértes, W.R. Schafer, PLoS Computat. Biol. 12, e1005283 (2016) ADSCrossRefGoogle Scholar
  35. 35.
    A.L. Hodgkin, A.F. Huxley, J. Physiol. 117, 500 (1952) CrossRefGoogle Scholar
  36. 36.
    W.S. McCulloch, W. Pitts, Bull. Math. Biophys. 5, 115 (1943) MathSciNetCrossRefGoogle Scholar
  37. 37.
    X. Li, Q. Chen, F. Xue, Philos. Trans. R. Soc. A 375, 20160286 (2017) ADSCrossRefGoogle Scholar
  38. 38.
    O.V. Maslennikov, D.S. Shchapin, V.I. Nekorkin, Philos. Trans. R. Soc. A 375, 20160288 (2017) ADSCrossRefGoogle Scholar
  39. 39.
    J.M. Fellous, C. Linster, Neural Comput. 10, 771 (1998) CrossRefGoogle Scholar
  40. 40.
    A.J. Yu, Computational models of neuromodulation, in Encyclopedia of Computational Neuroscience, edited by D. Jaeger, R. Jung (Springer, New York, 2014) Google Scholar
  41. 41.
    K. Doya, Nat. Neurosci. 11, 410 (2008) CrossRefGoogle Scholar
  42. 42.
    K. Doya, Neural Netw. 15, 495 (2002) CrossRefGoogle Scholar
  43. 43.
    R. Holca-Lamarre, J. Lücke, K. Obermayer, Front. Comput. Neurosci. 11, 54 (2017) CrossRefGoogle Scholar
  44. 44.
    G.J. Gutierrez, E. Marder, eNeuro 1, ENEURO.0009-14 (2014) CrossRefGoogle Scholar
  45. 45.
    T. O’Leary, A.H. Williams, J.S. Caplan, E. Marder, PNAS 110, E2645 (2013) CrossRefGoogle Scholar
  46. 46.
    E. Marder, M.L. Goeritz, A.G. Otopalik, Curr. Opin. Neurobiol. 31, 156 (2015) CrossRefGoogle Scholar
  47. 47.
    J.A. Reggia, E. Ruppin, D. Glanzman (eds.), Disorders of Brain, Behavior and Cognition: the Neurocomputational Perspective (Elsevier Science, Amsterdam, 1999) Google Scholar
  48. 48.
    R. Chaudhuri, I. Fiete, Nat. Neurosci. 19, 394 (2016) CrossRefGoogle Scholar
  49. 49.
    S. Tonegawa, M. Pignatelli, D.S. Roy, T.J. Ryan, Curr. Opin. Neurobiol. 35, 101 (2015) CrossRefGoogle Scholar
  50. 50.
    T.J. Ryan, D.S. Roy, M. Pignatelli, A. Arons, S. Tonegawa, Science 348, 1007 (2015) ADSCrossRefGoogle Scholar
  51. 51.
    H.K. Titley, N. Brunel, C. Hansel, Neuron 95, 19 (2017) CrossRefGoogle Scholar

Copyright information

© EDP Sciences, Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Centro de Investigación Operativa, Universidad Miguel HernándezElcheSpain

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