Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Hairong Lin, Chunhua Wang, Chengjie Chen, Yichuang Sun, Chao Zhou, Cong Xu, Qinghui Hong

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
50 Downloads (Pure)


Nowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.
Original languageEnglish
Pages (from-to)1-14
Number of pages13
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Early online date2 Jun 2021
Publication statusE-pub ahead of print - 2 Jun 2021


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