scholarly journals Cluster Burst Synchronization in A Scale-Free Network of Inhibitory Bursting Neurons

2018 ◽  
Author(s):  
Sang-Yoon Kim ◽  
Woochang Lim

We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced cluster burst synchronization by varying the average coupling strength J0. For sufficiently small J0, non-cluster desynchronized states exist. However, when passing a critical point , the whole population is segregated into 3 clusters via a constructive role of synaptic inhibition to stimulate dynamical clustering between individual burstings, and thus 3-cluster desynchronized states appear. As J0 is further increased and passes a lower threshold , a transition to 3-cluster burst synchronization occurs due to another constructive role of synaptic inhibition to favor population synchronization. In this case, HR neurons in each cluster exhibit burst synchronization. However, as J0 passes an intermediate threshold , HR neurons begin to make intermittent hoppings between the 3 clusters. Due to the intermittent intercluster hoppings, the 3 clusters are integrated into a single one. In spite of break-up of the 3 clusters, (non-cluster) burst synchronization persists in the whole population, which is well visualized in the raster plot of burst onset times where bursting stripes (composed of burst onset times and indicating burst synchronization) appear successively. With further increase in J0, intercluster hoppings are intensified, and bursting stripes also become smeared more and more due to a destructive role of synaptic inhibition to spoil the burst synchronization. Eventually, when passing a higher threshold a transition to desynchronization occurs via complete overlap between the bursting stripes. Finally, we also investigate the effects of stochastic noise on both 3-cluster burst synchronization and intercluster hoppings.






Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 932
Author(s):  
Yutaka Okabe ◽  
Akira Shudo

Mathematical models of the spread of epidemic diseases are studied, paying special attention to networks. We treat the Susceptible-Infected-Recovered (SIR) model and the Susceptible-Exposed-Infectious-Recovered (SEIR) model described by differential equations. We perform microscopic numerical simulations for corresponding epidemic models on networks. Comparing a random network and a scale-free network for the spread of the infection, we emphasize the role of hubs in a scale-free network. We also present a simple derivation of the exact solution of the SIR model.





2009 ◽  
Vol 29 (5) ◽  
pp. 1230-1232
Author(s):  
Hao RAO ◽  
Chun YANG ◽  
Shao-hua TAO


2012 ◽  
Vol 39 (6) ◽  
pp. 581-590 ◽  
Author(s):  
Ming ZHENG ◽  
Yan-Xin HUANG ◽  
Wei SHEN ◽  
Yi ZHONG ◽  
Jia-Nan WU ◽  
...  


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