Advertisement

Neurodynamics on Up and Down Transitions of Membrane Potential: From Single Neuron to Network

  • Xuying Xu
  • Rubin Wang
  • Jianting Cao
Conference paper
Part of the Advances in Cognitive Neurodynamics book series (ICCN)

Abstract

The phenomenon of up and down transition is an important characteristic of spontaneous brain activity, which happens in various levels of the nervous system. In level of membrane potentials, it shows spontaneous periodic transitions between two subthreshold preferred stable states. Here, we have studied the mechanisms and characteristics of up and down transitions of membrane potentials from single neuron to network model. Furthermore, we have developed the model by considering both excitatory and inhibitory neurons and introducing synaptic dynamics into network model. Based on this model, we studied the influence of intrinsic characteristics and network parameters on up and down activities. The main output of this study is that the network parameters have little impact on these spontaneous periodic up and down transitions. However, the intrinsic currents were found to play a leading role in the process. In this regard, we expect to explain the dynamics of up and down transitions and to lay the foundation for future work on the role of these transitions in cortex activity.

Keywords

Up and down transitions Spontaneous activity Ionic channel Dynamical characteristics 

Notes

Acknowledgments

This study is supported by the National Natural Science Foundation of China (No. 11232005) and the Fundamental Research Funds for the Central Universities (No. 222201714020).

References

  1. 1.
    Anderson, J., Lampl, I., Reichova, I., Carandini, M., Ferster, D.: Stimulus dependence of two-state fluctuations of membrane potential in cat visual cortex. Nat. Neurosci. 3, 617–621 (2000)CrossRefPubMedGoogle Scholar
  2. 2.
    Lampl, I., Reichova, I., Ferster, D.: Synchronous membrane potential fluctuations in neurons of the cat visual cortex. Neuron. 22, 361–374 (1999)CrossRefPubMedGoogle Scholar
  3. 3.
    Steriade, M., Nunez, A., Amzica, F.: Intracellular analysis of relations between the slow (< 1 Hz) neocortical oscillation and other sleep rhythms of the electroencephalogram. J. Neurosci. 13, 3266–3283 (1993)CrossRefPubMedGoogle Scholar
  4. 4.
    Petersen, C.C.H., Hahn, T.T.G., Mehta, M., Grin-Vald, A., Sakmann, B.: Interaction of sensory responses with spontaneous depolarization in layer 2/3 barrel cortex. Proc. Natl. Acad. Sci. U. S. A. 100, 13638–13643 (2003)CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Parga, N., Abbott, L.F.: Network model of spontaneous activity exhibiting synchronous transitions between up and down states. Neuroscience. 1, 57–66 (2007)Google Scholar
  6. 6.
    Loewenstein, Y., Mahon, S., Chadderton, P., Kitamura, K., Sompolinsky, H., Yarom, Y., Häusser, M.: Bistability of cerebellar Purkinje cells modulated by sensory stimulation. Neuroscience. 8, 202–211 (2005)PubMedGoogle Scholar
  7. 7.
    Xu, X., Wang, R.: Neurodynamics of up and down transitions in a single neuron. Cogn. Neurodyn. 8, 509–515 (2014)CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Xu, X., Wang, R.: Neurodynamics of up and down transitions in network model. Abstr. Appl. Anal. 2013, 486178 (2013)Google Scholar
  9. 9.
    Xu, X., Ni, L., Wang, R.: A neural network model of spontaneous up and down transitions. Nonlinear Neurodynamics. 84, 1541–1551 (2016)CrossRefGoogle Scholar
  10. 10.
    Ermentrout, G.B., Terman, D.H.: Mathematical Foundations of Neuroscience. Springer, New York (2010)CrossRefGoogle Scholar
  11. 11.
    Xu, X., Ni, L., Wang, R.: Synchronous transitions of up and down states in a network model based on stimulations. J. Theor. Biol. 412, 130–137 (2017)CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Xuying Xu
    • 1
  • Rubin Wang
    • 1
  • Jianting Cao
    • 2
    • 3
  1. 1.Institute of Cognitive Neurodynamics, East China University of Science and TechnologyShanghaiChina
  2. 2.Saitama Institute of TechnologyFukayaJapan
  3. 3.Brain Science Institute, RIKENWakoJapan

Personalised recommendations