Advertisement

The European Physical Journal Special Topics

, Volume 227, Issue 10–11, pp 1063–1076 | Cite as

Stimulus-evoked activity in clustered networks of stochastic rate-based neurons

  • Igor FranovićEmail author
  • Vladimir KlinshovEmail author
Regular Article
Part of the following topical collections:
  1. Advances in Nonlinear Dynamics of Complex Networks: Adaptivity, Stochasticity, Delays

Abstract

Understanding the effect of network connectivity patterns on the relation between the spontaneous and the stimulus-evoked network activity has become one of the outstanding issues in neuroscience. We address this problem by considering a clustered network of stochastic rate-based neurons influenced by external and intrinsic noise. The bifurcation analysis of an effective model of network dynamics, comprised of coupled mean-field models representing each of the clusters, is used to gain insight into the structure of metastable states characterizing the spontaneous and the induced dynamics. We show that the induced dynamics strongly depends on whether the excitation is aimed at a certain cluster or the same fraction of randomly selected units, whereby the targeted stimulation reduces macroscopic variability by biasing the network toward a particular collective state. The immediate effect of clustering on the induced dynamics is established by comparing the excitation rates of a clustered and a homogeneous random network.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    T.T.G. Hahn, J.M. McFarland, S. Berberich, B. Sakmann, M.R. Mehta, Nat. Neurosci. 15, 1531 (2012) CrossRefGoogle Scholar
  2. 2.
    G. Buzsáki, C.A. Anastassiou, C. Koch, Nat. Rev. Neurosci. 13, 407 (2012) CrossRefGoogle Scholar
  3. 3.
    V.V. Vyazovskiy, K.D. Harris, Nat. Rev. Neurosci. 14, 443 (2013) CrossRefGoogle Scholar
  4. 4.
    M.R. Cohen, A. Kohn, Nat. Neurosci. 14, 811 (2011) CrossRefGoogle Scholar
  5. 5.
    A. Litwin-Kumar, B. Doiron, Nat. Neurosci. 15, 1498 (2012) CrossRefGoogle Scholar
  6. 6.
    C.C.H. Petersen, T.T.G. Hahn, M. Mehta, A. Grinvald, B. Sakmann, Proc. Natl. Acad. Sci. U.S.A. 100, 13638 (2003) ADSCrossRefGoogle Scholar
  7. 7.
    D. Millman, S. Mihalas, A. Kirkwood, E. Niebur, Nat. Phys. 6, 801 (2010) CrossRefGoogle Scholar
  8. 8.
    J. Anderson et al., Nat. Neurosci. 3, 617 (2000) CrossRefGoogle Scholar
  9. 9.
    R. Cossart, D. Aronov, R. Yuste, Nature 423, 283 (2003) ADSCrossRefGoogle Scholar
  10. 10.
    A. Luczak, P. Barthó, K.D. Harris, Neuron 62, 413 (2009) CrossRefGoogle Scholar
  11. 11.
    A. Luczak, P. Barthó, K.D. Harris, J. Neurosci. 33, 1684 (2013) CrossRefGoogle Scholar
  12. 12.
    D.L. Ringach, Curr. Opin. Neurobiol. 19, 439 (2009) CrossRefGoogle Scholar
  13. 13.
    G. Deco, E. Hugues, PLoS Comput. Biol. 8, e1002395 (2012) ADSCrossRefGoogle Scholar
  14. 14.
    D. Ji, M.A. Wilson, Nat. Neurosci. 10, 100 (2007) CrossRefGoogle Scholar
  15. 15.
    S. Diekelmann, J. Born, Nat. Rev. Neurosci. 11, 114 (2010) CrossRefGoogle Scholar
  16. 16.
    D. Miyamoto et al., Science 352, 1315 (2016) ADSCrossRefGoogle Scholar
  17. 17.
    G. Rothschild, E. Eban, L.M. Frank, Nat. Neurosci. 20, 251 (2017) CrossRefGoogle Scholar
  18. 18.
    J.L. Vincent et al. Nature 447, 83 (2007) ADSCrossRefGoogle Scholar
  19. 19.
    V. Pasquale, S. Martinoia, M. Chiappalone, Sci. Rep. 7, 9080 (2017) ADSCrossRefGoogle Scholar
  20. 20.
    B. Doiron, A. Litwin-Kumar, Front. Comput. Neurosci. 8, 56 (2014) CrossRefGoogle Scholar
  21. 21.
    S. Song, P. Sjöström, M. Reigl, S. Nelson, D. Chklovskii, PLoS Biol. 3, e68 (2005) CrossRefGoogle Scholar
  22. 22.
    S. Lefort, C. Tomm, J.-C.F. Sarria, C.C.H. Petersen, J.C. Floyd Sarria, C.C.H. Petersen, Neuron 61, 301 (2009) CrossRefGoogle Scholar
  23. 23.
    R. Perin, M. Telefont, H. Markram, Front. Neuroanat. 7, 1 (2013) CrossRefGoogle Scholar
  24. 24.
    V.V. Klinshov, J.N. Teramae, V.I. Nekorkin, T. Fukai, PLoS One 9, e94292 (2014) ADSCrossRefGoogle Scholar
  25. 25.
    S.B. Hofer, H. Ko, B. Pichler, J. Vogelstein, H. Ros et al., Nat. Neurosci. 14, 1045 (2011) CrossRefGoogle Scholar
  26. 26.
    H. Ko, S.B. Hofer, B. Pichler, K.A. Buchanan, P.J. Sjöström, T.D. Mrsic-Flogel, Nature 473, 87 (2011) ADSCrossRefGoogle Scholar
  27. 27.
    I. Franović, V. Klinshov, Chaos 28, 023111 (2018) ADSMathSciNetCrossRefGoogle Scholar
  28. 28.
    I. Franović, V. Klinshov, Europhys. Lett. 116, 48002 (2016) ADSCrossRefGoogle Scholar
  29. 29.
    V. Klinshov, I. Franović, Phys. Rev. E 92, 062813 (2015) ADSMathSciNetCrossRefGoogle Scholar
  30. 30.
    H. Hasegawa, Phys. Rev. E 75, 051904 (2007) ADSMathSciNetCrossRefGoogle Scholar
  31. 31.
    R.A. Anderson, S. Musallam, B. Pesaran, Curr. Opin. Neurobiol. 14, 720 (2004) CrossRefGoogle Scholar
  32. 32.
    B. Lindner, J. Garcia-Ojalvo, A. Neiman, L. Schimansky-Geier, Phys. Rep. 392, 321 (2004) ADSCrossRefGoogle Scholar
  33. 33.
    M.A. Zaks, X. Sailer, L. Schimansky-Geier, A.B. Neiman, Chaos 15, 026117 (2005) ADSMathSciNetCrossRefGoogle Scholar
  34. 34.
    I. Franović, K. Todorović, N. Vasović, N. Burić, Phys. Rev. E 87, 012922 (2013) ADSCrossRefGoogle Scholar
  35. 35.
    I. Franović, K. Todorović, N. Vasović, N. Burić, Phys. Rev. E 89, 022926 (2014) ADSCrossRefGoogle Scholar
  36. 36.
    A.N. Burkitt, Biol. Cybern. 95, 1 (2006) MathSciNetCrossRefGoogle Scholar
  37. 37.
    S. Ciuchi, F. de Pasquale, B. Spagnolo, Phys. Rev. E 47, 3915 (1993) ADSCrossRefGoogle Scholar
  38. 38.
    N.V. Agudov, A.A. Dubkov, B. Spagnolo, Physica A 325, 144 (2003) ADSMathSciNetCrossRefGoogle Scholar
  39. 39.
    G. Augello, D. Valentia, B. Spagnolo, Eur. Phys. J. B 78, 225 (2010) ADSCrossRefGoogle Scholar
  40. 40.
    L. Mazzucato, A. Fontanini, G. La Camera, J. Neurosci. 35, 8214 (2015) CrossRefGoogle Scholar
  41. 41.
    I.-C. Lin, M. Okun, M. Carandini, K.D. Harris, Neuron 87, 644 (2015) 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.Scientific Computing Laboratory, Center for the Study of Complex Systems, Institute of Physics Belgrade, University of BelgradeBelgradeSerbia
  2. 2.Institute of Applied Physics of the Russian Academy of SciencesNizhny NovgorodRussia

Personalised recommendations