Complex patterns in networks of hyperexcitable neurons

Craig Schindewolf, Dongwook Kim, Andrea Bel, Horacio G. Rotstein

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Complex patterns in neuronal networks emerge from the cooperative activity of the participating neurons, synaptic connectivity and network topology. Several neuron types exhibit complex intrinsic dynamics due to the presence of nonlinearities and multiple time scales. In this paper we extend previous work on hyperexcitability of neuronal networks, a hallmark of epileptic brain seizure generation, which results from the net imbalance between excitation and inhibition and the ability of certain neuron types to exhibit abrupt transitions between low and high firing frequency regimes as the levels of recurrent AMPA excitation change. We examine the effect of different topologies and connection delays on the hyperexcitability phenomenon in networks having recurrent synaptic AMPA (fast) excitation (in the absence of synaptic inhibition) and demonstrate the emergence of additional time scales.

Original languageEnglish (US)
Pages (from-to)71-82
Number of pages12
JournalTheoretical Computer Science
Volume633
DOIs
StatePublished - Jun 20 2016

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Neuronal networks
  • Synchronization

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