@misc{17330,
  author = {Eirill Hauge and Marte S{\ae}tra and Gaute Einevoll and Marie Rognes and Geir Halnes},
  title = {Neuronal synchronization through electrochemical ephaptic coupling},
  abstract = {Neuronal synchronization plays an important role in memory formation, while deviations in the synchrony are seen in a wide range of neuropsychiatric disorders. Neuronal networks synchronize primarily through synapses, though contributions from indirect neuronal communication through the extracellular space (ECS), known as ephaptic coupling, are observed under certain conditions. Furthering our knowledge of the dynamics between the neuronal network activity and the ECS is vital to better understand brain function, especially in neurodegenerative diseases associated with deviations from baseline extracellular ion concentrations. In-silico studies often focus on ephaptic coupling via local field potentials, as most computational models assume that ion concentrations in the ECS are constant. The chemical ephaptic effects, caused by changes in ECS ion concentrations due to transmembrane fluxes, remain largely unexplored. In this study, we extend an ion-conserving electrodiffusive two-compartment model of a neuron to describe a small network of excitatory pyramidal neurons sharing a common extracellular space. The model predicts the time evolution of the intracellular and extracellular concentrations of key ion species (Na+, K+, Cl-, and Ca2+), as well as the intracellular and extracellular potentials. With this model, we may simultaneously account for the effects that synaptic, and both electric and chemical ephaptic coupling have on neural synchrony. We quantify and compare the contribution to ephaptic effects made by changes in ionic concentrations versus variations in the electric potentials. Our studies provide insight into changes in network spike patterns due to the interplay between membrane currents and the ECS under various conditions.},
  year = {2023},
  journal = {Bernstein Conference, 2023 Berlin, Germany},
  publisher = {Bernstein Netzwerk Computational Neuroscience},
  address = {Bernstein Conference, 2023 Berlin, Germany},
}