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

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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.

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Acknowledgements

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.

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Authors and Affiliations

  1. Department of Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA

    Maxim Komarov, Giri Krishnan & Maxim Bazhenov

  2. Centre de recherche de l’Institut universitaire en santé mentale de Québec (CRIUSMQ), Local F-6500, 2601 de la Canardière, QC, Québec, G1J2G3, Canada

    Sylvain Chauvette & Igor Timofeev

  3. BioCircuits Institute, University of California, San Diego 9500 Gilman Drive, La Jolla, CA, 92093-0328, USA

    Nikolai Rulkov

  4. Department of Psychiatry and Neuroscience, Université Laval, Québec, Canada

    Igor Timofeev

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  1. Maxim Komarov
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  3. Sylvain Chauvette
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Correspondence to Giri Krishnan.

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In this study, we developed a novel class of the computationally efficient neuron models that allow for large-scale simulation (based on the millions of neurons and billions of connections) of sleep and wake brain dynamics. This new model implements intrinsic properties similar to those found in conductance based neuronal models. Analysis of the cortical network dynamics during slow-wave sleep, based on the new model, revealed a close agreement between model predictions and experimental data. The study predicted the critical role of synaptic noise and intrinsic cellular adaptation for spike sequence replay during sleep related memory consolidation.

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Komarov, M., Krishnan, G., Chauvette, S. et al. New class of reduced computationally efficient neuronal models for large-scale simulations of brain dynamics. J Comput Neurosci 44, 1–24 (2018). https://doi.org/10.1007/s10827-017-0663-7

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