Guiding Synchrony through Random Networks

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Sven Jahnke - , Max Planck Institute for Dynamics and Self-Organization, Bernstein Center for Computational Neuroscience Göttingen, University of Göttingen (Author)
  • Marc Timme - , Max Planck Institute for Dynamics and Self-Organization, Bernstein Center for Computational Neuroscience Göttingen, University of Göttingen (Author)
  • Raoul Martin Memmesheimer - , Radboud University Nijmegen (Author)

Abstract

Sparse random networks contain structures that can be considered as diluted feed-forward networks. Modeling of cortical circuits has shown that feed-forward structures, if strongly pronounced compared to the embedding random network, enable reliable signal transmission by propagating localized (subnetwork) synchrony. This assumed prominence, however, is not experimentally observed in local cortical circuits. Here, we show that nonlinear dendritic interactions, as discovered in recent single-neuron experiments, naturally enable guided synchrony propagation already in random recurrent neural networks that exhibit mildly enhanced, biologically plausible substructures.

Details

Original languageEnglish
Article number041016
JournalPhysical Review X
Volume2
Issue number4
Publication statusPublished - 13 Dec 2012
Peer-reviewedYes
Externally publishedYes

External IDs

ORCID /0000-0002-5956-3137/work/142242481

Keywords

ASJC Scopus subject areas

Keywords

  • Biological physics, Complex systems