Synchronised firing patterns in a random network of adaptive exponential integrate-and-fire neuron model. (June 2017)

• Record Type: Journal Article
• Authors: Borges, F.S.; Protachevicz, P.R.; Lameu, E.L.; Bonetti, R.C.; Iarosz, K.C.; Caldas, I.L.; Baptista, M.S.; Batista, A.M.
• Is Part Of: Neural networks. Volume 90(2017)
• Page Start: 1
• Page End: 7
• Publication Date: 2017-06
• Subjects: Synchronisation -- Integrate-and-fire -- Network; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2017.03.005
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: We have studied neuronal synchronisation in a random network of adaptive exponential integrate-and-fire neurons. We study how spiking or bursting synchronous behaviour appears as a function of the coupling strength and the probability of connections, by constructing parameter spaces that identify these synchronous behaviours from measurements of the inter-spike interval and the calculation of the order parameter. Moreover, we verify the robustness of synchronisation by applying an external perturbation to each neuron. The simulations show that bursting synchronisation is more robust than spike synchronisation.