Emergence of global synchronization in directed excitatory networks of type I neurons

Abolfazl Ziaeemehr1, Mina Zarei1,2* & Aida Sheshbolouki3

1. Institute of Advanced Studies in Basic Sciences (IASBS), Department of Physics, Zanjan, 45137-66731, Iran.
2. Institute for Research in Fundamental Sciences (IPM), School of Computer Science, Tehran, 19395-5531, Iran.
3. University of Waterloo, Cheriton School of Computer Science, Waterloo, N2L3G1, Canada.

The collective behaviour of neural networks depends on the cellular and synaptic properties of the neurons. the phase-response curve (pRc) is an experimentally obtainable measure of cellular properties that quantifies the shift in the next spike time of a neuron as a function of the phase at which stimulus is delivered to that neuron. The neuronal PRCs can be classified as having either purely positive values (type I) or distinct positive and negative regions (type II). Networks of type 1 PRCs tend not to synchronize via mutual excitatory synaptic connections. We study the synchronization properties of identical type i and type ii neurons, assuming unidirectional synapses. performing the linear stability analysis and the numerical simulation of the extended Kuramoto model, we show that feedforward loop motifs favour synchronization of type i excitatory and inhibitory neurons, while feedback loop motifs destroy their synchronization tendency. Moreover, large directed networks, either without feedback motifs or with many of them, have been constructed from the same undirected backbones, and a high synchronization level is observed for directed acyclic graphs with type i neurons. it has been shown that, the synchronizability of type i neurons depends on both the directionality of the network connectivity and the topology of its undirected backbone. the abundance of feedforward motifs enhances the synchronizability of the directed acyclic graphs.

How to cite the paper:

@article{ziaeemehr2020emergence,
  title={Emergence of global synchronization in directed excitatory networks of type I neurons},
  author={Ziaeemehr, Abolfazl and Zarei, Mina and Sheshbolouki, Aida},
  journal={Scientific reports},
  volume={10},
  number={1},
  pages={1--11},
  year={2020},
  publisher={Nature Publishing Group}
}