Synchronization from Second Order Network Connectivity Statistics
By Liqiong Zhao, Bryce Beverlin, Theoden I. Netoff, Duane Q. Nykamp. 2011
106 citations recorded by the source corpus.
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.
Subfields: Cognitive Neuroscience, Computer Networks and Communications
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
Citations in the corpus
- Structured Connectivity in Cerebellar Inhibitory Networks (2014, background): “Zhao et al. (2011) showed that deviations from random networks caused by overrepresenting different network motifs involving two connections in either a divergent, convergent, or chain configuration can have opposing effects on synchrony.”
- Synchronization and desynchronization in epilepsy: controversies and hypotheses (2012, background): “2; Li et al. 2007; Muller et al. 2008) and random networks (Zhao et al. 2011) demonstrate that our conceptual understanding of statistical synchrony continues to advance, and will further change our concept of synchrony in epileptic seizures.”