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A proposed role for electrical coupling in the neocortical slow oscillation

  • Roger D. Traub EMAIL logo , Andreas Draguhn , Diego Contreras and Mark O. Cunningham
Published/Copyright: May 7, 2025
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Abstract

We constructed a computational thalamocortical network model for study of the neocortical slow oscillation. It incorporated a number of neuronal types, both excitatory and inhibitory, each model neuron simulated as a multicompartment entity with numerous membrane conductances. As in previous experimental and modeling studies, simulated slow oscillations primarily depended on recurrently connected deep intrinsic bursting (IB) pyramidal cells, with NMDA receptors being critical as well as intrinsic membrane conductances (e.g. persistent Na+); and with repolarization to the Down state dependent on intrinsic (slow Ca2+-dependent K+) and synaptic (GABAB receptor mediated) conductances. Furthermore, however, we now can account for additional features of the slow oscillation: the frequent occurrence of spikelets, the presence of very fast ripple-like oscillations, and the transition to so-called fast runs (10 to ∼20 Hz bursty oscillations). These latter phenomena depended in our model on electrical coupling via gap junctions between pyramidal neurons. The importance of gap junctions is supported by previous experimental data on the ripple-blocking effect of halothane, as well as by data from the in vitro hippocampus.


Corresponding author: Roger D. Traub, Exploratory Research, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA; Department of Neuroscience, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; and Discipline of Physiology, School of Medicine, Trinity College Dublin, University of Dublin, 152-160 Pearse St., Dublin 2, Ireland, E-mail:

Award Identifier / Grant number: 20/FEP-P/8613

Award Identifier / Grant number: DR 326/15-1 project 661624

Award Identifier / Grant number: NSF IIS-2207707

Funding source: IBM Exploratory Research Council

Award Identifier / Grant number: N.A.

Acknowledgments

We thank Shu-Ping Chang and Robert Walkup for critical help with computing issues.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Author contributions: RDT performed simulations. RDT, DC, AD and MOC analyzed data and wrote the paper. All authors accept responsibility for the entire work and approve its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: Not applicable.

  5. Conflict of interest: The authors declare no conflicts of interest regarding this article.

  6. Research funding: RDT was funded by the IBM Exploratory Research Council. MOC was funded by Science Foundation Ireland (Frontier for the Future project award 20/FEP-P/8613). AD was funded by DFG (Deutsche Forschungsgemeinschaft), DR 326/15-1, project number 661624). DC was funded by the National Science Foundation, NSF IIS-2207707.

  7. Data availability: Source code will be made available in ModelDB upon acceptance.

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Received: 2025-02-04
Accepted: 2025-04-11
Published Online: 2025-05-07

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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