Biological Cybernetics

, Volume 92, Issue 2, pp 71–81 | Cite as

Inhibition modifies the effects of slow calcium-activated potassium channels on epileptiform activity in a neuronal network model

  • K.-H. Yang
  • Piotr J. Franaszczuk
  • Gregory K. Bergey
Article
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Abstract

Generation of epileptiform activity typically results from a change in the balance between network excitation and inhibition. Experimental evidence indicates that alterations of either synaptic activity or intrinsic membrane properties can produce increased network excitation. The slow Ca2+-activated K+ currents (sIAHP) are important modulators of neuronal firing rate and excitability and have important established and potential roles in epileptogenesis. While the effects of changes in sIAHP on individual neuronal excitability are readily studied and well established, the effects of such changes on network behavior are less well known. The experiments here utilize a defined small network model of multicompartment pyramidal cells and an inhibitory interneuron to study the effects of changes in sIAHP on network behavior. The benefits of this model system include the ability to observe activity in all cells in a network and the effects of interactions of multiple simultaneous influences. In the model with no inhibitory interneuron, increasing sIAHP results in progressively decreasing burst activity. Adding an inhibitory interneuron changes the observed effects; at modest inhibitory strengths, increasing sIAHP in all network neurons actually results in increased network bursting (except at very high values). The duration of the burst activity is influenced by the length of delay in a feedback loop, with longer loops resulting in more prolonged bursting. These observations illustrate that the study of potential antiepileptogenic membrane effects must be extended to realistic networks. Network inhibition can dramatically alter the observations seen in pure excitatory networks.

Keywords

Firing Rate Neuronal Excitability Neuronal Firing Burst Activity Inhibitory Interneuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgments.

This research is supported by NIH grant NS38958. A preliminary version of this work was presented at the Computational Neuroscience 2003 meeting.

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Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • K.-H. Yang
    • 1
  • Piotr J. Franaszczuk
    • 1
  • Gregory K. Bergey
    • 1
  1. 1.Department of Neurology, Johns Hopkins Epilepsy CenterJohns Hopkins University School of MedicineBaltimoreUSA

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