A role for electrotonic coupling between cortical pyramidal cells

Jennifer Crodelle, Doug Zhou, Gregor Kovačič, David Cai

Research output: Contribution to journalArticle

Abstract

Many brain regions communicate information through synchronized network activity. Electrical coupling among the dendrites of interneurons in the cortex has been implicated in forming and sustaining such activity in the cortex. Evidence for the existence of electrical coupling among cortical pyramidal cells, however, has been largely absent. A recent experimental study measured properties of electrical connections between pyramidal cells in the cortex deemed “electrotonic couplings.” These junctions were seen to occur pair-wise, sparsely, and often coexist with electrically-coupled interneurons. Here, we construct a network model to investigate possible roles for these rare, electrotonically-coupled pyramidal-cell pairs. Through simulations, we show that electrical coupling among pyramidal-cell pairs significantly enhances coincidence-detection capabilities and increases network spike-timing precision. Further, a network containing multiple pairs exhibits large variability in its firing pattern, possessing a rich coding structure.

Original languageEnglish (US)
Article number33
JournalFrontiers in Computational Neuroscience
Volume13
DOIs
StatePublished - May 24 2019

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Pyramidal Cells
Interneurons
Dendrites
Brain

Keywords

  • Coincidence detection
  • Electrotonic coupling
  • Information processing
  • Pyramidal cells
  • Synchrony

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Cellular and Molecular Neuroscience

Cite this

A role for electrotonic coupling between cortical pyramidal cells. / Crodelle, Jennifer; Zhou, Doug; Kovačič, Gregor; Cai, David.

In: Frontiers in Computational Neuroscience, Vol. 13, 33, 24.05.2019.

Research output: Contribution to journalArticle

Crodelle, Jennifer ; Zhou, Doug ; Kovačič, Gregor ; Cai, David. / A role for electrotonic coupling between cortical pyramidal cells. In: Frontiers in Computational Neuroscience. 2019 ; Vol. 13.
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