### Abstract

Original language | English |
---|---|

Pages (from-to) | 1-7 |

Number of pages | 7 |

Journal | Neural Networks |

Volume | 90 |

Early online date | 16 Mar 2017 |

DOIs | |

Publication status | Published - Jun 2017 |

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### Keywords

- integrate-and -fire
- network
- synchronisation

### Cite this

*Neural Networks*,

*90*, 1-7. https://doi.org/10.1016/j.neunet.2017.03.005

**Synchronised firing patterns in a random network of adaptive exponential integrate-and-fire neuron model.** / Borges, F.S.; Protachevicz, P.R.; Lameu, E.L.; Bonetti, R.C.; Larosz, K.C.; Caldas, I.L.; Baptista, M.S.; Batista, A.M.

Research output: Contribution to journal › Article

*Neural Networks*, vol. 90, pp. 1-7. https://doi.org/10.1016/j.neunet.2017.03.005

}

TY - JOUR

T1 - Synchronised firing patterns in a random network of adaptive exponential integrate-and-fire neuron model

AU - Borges, F.S.

AU - Protachevicz, P.R.

AU - Lameu, E.L.

AU - Bonetti, R.C.

AU - Larosz, K.C.

AU - Caldas, I.L.

AU - Baptista, M.S.

AU - Batista, A.M.

N1 - Acknowledgements This study was possible by partial financial support from the following Brazilian government agencies: CNPq, CAPES, and FAPESP (2011/19296-1 and 2015/07311-7). We also wish thank Newton Fund and COFAP.

PY - 2017/6

Y1 - 2017/6

N2 - 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.

AB - 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.

KW - integrate-and -fire

KW - network

KW - synchronisation

U2 - 10.1016/j.neunet.2017.03.005

DO - 10.1016/j.neunet.2017.03.005

M3 - Article

VL - 90

SP - 1

EP - 7

JO - Neural Networks

JF - Neural Networks

SN - 0893-6080

ER -