Journal of Computational Neuroscience

, Volume 44, Issue 1, pp 1–24 | Cite as

New class of reduced computationally efficient neuronal models for large-scale simulations of brain dynamics

  • Maxim Komarov
  • Giri Krishnan
  • Sylvain Chauvette
  • Nikolai Rulkov
  • Igor Timofeev
  • Maxim Bazhenov


During slow-wave sleep, brain electrical activity is dominated by the slow (< 1 Hz) electroencephalogram (EEG) oscillations characterized by the periodic transitions between active (or Up) and silent (or Down) states in the membrane voltage of the cortical and thalamic neurons. Sleep slow oscillation is believed to play critical role in consolidation of recent memories. Past computational studies, based on the Hodgkin-Huxley type neuronal models, revealed possible intracellular and network mechanisms of the neuronal activity during sleep, however, they failed to explore the large-scale cortical network dynamics depending on collective behavior in the large populations of neurons. In this new study, we developed a novel class of reduced discrete time spiking neuron models for large-scale network simulations of wake and sleep dynamics. In addition to the spiking mechanism, the new model implemented nonlinearities capturing effects of the leak current, the Ca2+ dependent K+ current and the persistent Na+ current that were found to be critical for transitions between Up and Down states of the slow oscillation. We applied the new model to study large-scale two-dimensional cortical network activity during slow-wave sleep. Our study explained traveling wave dynamics and characteristic synchronization properties of transitions between Up and Down states of the slow oscillation as observed in vivo in recordings from cats. We further predict a critical role of synaptic noise and slow adaptive currents for spike sequence replay as found during sleep related memory consolidation.


Slow-wave sleep oscillations Large-scale simulations Up and down states 



This work was supported by grants from ONR (MURI: N000141310672), NIH (MH099645) and Canadian Institutes of Health Research (MOP-136969, MOP-136967). MK and NR also appreciate partial support from ONR grant N00014-16-1-2252.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Maxim Komarov
    • 1
  • Giri Krishnan
    • 1
  • Sylvain Chauvette
    • 2
  • Nikolai Rulkov
    • 3
  • Igor Timofeev
    • 2
    • 4
  • Maxim Bazhenov
    • 1
  1. 1.Department of MedicineUniversity of California San DiegoLa JollaUSA
  2. 2.Centre de recherche de l’Institut universitaire en santé mentale de Québec (CRIUSMQ)QCCanada
  3. 3.BioCircuits InstituteUniversity of CaliforniaLa JollaUSA
  4. 4.Department of Psychiatry and NeuroscienceUniversité LavalQuébecCanada

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