Synchronization from second order network connectivity statistics

Liqiong Zhao, Bryce Beverlin, Theoden Netoff, Duane Q. Nykamp

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We investigate how network structure can influence the tendency for a neuronal network to synchronize, or its synchronizability, independent of the dynamical model for each neuron. The synchrony analysis takes advantage of the framework of second order networks, which defines four second order connectivity statistics based on the relative frequency of two-connection network motifs. The analysis identifies two of these statistics, convergent connections, and chain connections, as highly influencing the synchrony. Simulations verify that synchrony decreases with the frequency of convergent connections and increases with the frequency of chain connections. These trends persist with simulations of multiple models for the neuron dynamics and for different types of networks. Surprisingly, divergent connections, which determine the fraction of shared inputs, do not strongly influence the synchrony. The critical role of chains, rather than divergent connections, in influencing synchrony can be explained by their increasing the effective coupling strength. The decrease of synchrony with convergent connections is primarily due to the resulting heterogeneity in firing rates.

Original languageEnglish (US)
Article number28
JournalFrontiers in Computational Neuroscience
StatePublished - Jul 8 2011

Bibliographical note

Publisher Copyright:
© 2011 Zhao, Beverlin, Netoff and Nykamp.


  • Common input
  • Correlations
  • Degree distribution
  • Maximum entropy
  • Neuronal networks
  • Synchrony


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