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J Comput Neurosci. 2018 Feb;44(1):1-24. doi: 10.1007/s10827-017-0663-7. Epub 2017 Dec 12.

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

Author information

1
Department of Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
2
Department of Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA. giri.prashanth@gmail.com.
3
Centre de recherche de l'Institut universitaire en santé mentale de Québec (CRIUSMQ), Local F-6500, 2601 de la Canardière, QC, Québec, G1J2G3, Canada.
4
BioCircuits Institute, University of California, San Diego 9500 Gilman Drive, La Jolla, CA, 92093-0328, USA.
5
Department of Psychiatry and Neuroscience, Université Laval, Québec, Canada.

Abstract

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.

KEYWORDS:

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

PMID:
29230640
DOI:
10.1007/s10827-017-0663-7
[Indexed for MEDLINE]

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