Tonic-clonic transitions in computer simulation

William W. Lytton, Ahmet Omurtag

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

Network simulations can help identify underlying mechanisms of epileptic activity that are hard to isolate in biologic preparations. To be useful, simulations must be sufficiently realistic to make possible biologic and clinical prediction. This requirement for large networks of sufficiently detailed neurons raises challenges both with regard to computational load and the difficulty of obtaining insights with large numbers of free parameters and the large amounts of generated data. The authors have addressed these problems by simulating computationally manageable networks of moderate size consisting of 1,000 to 3,000 neurons with multiple intrinsic and synaptic properties. Experiments on these simulations demonstrated the presence of epileptiform behavior in the form of repetitive high-intensity population events (clonic behavior) or latch-up with near maximal activity (tonic behavior). Intrinsic neuronal excitability is not always a predictor of network epileptiform activity but may paradoxically produce antiepileptic effects, depending on the settings of other parameters. Several simulations revealed the importance of random coincident inputs to shift a network from a low-activation to a high-activation epileptiform state. Finally, a simulated anticonvulsant acting on excitability tended to preferentially decrease tonic activity.

Original languageEnglish (US)
Pages (from-to)175-181
Number of pages7
JournalJournal of Clinical Neurophysiology
Volume24
Issue number2
DOIs
StatePublished - Apr 2007

ASJC Scopus subject areas

  • Physiology
  • Neurology
  • Clinical Neurology
  • Physiology (medical)

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