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        Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

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        Author
        Lin, Hairong
        Wang, Chunhua
        Chen, Chengjie
        Sun, Yichuang
        Zhou, Chao
        Xu, Cong
        Hong , Qinghui
        Attention
        2299/24580
        Abstract
        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.
        Publication date
        2021-06-02
        Published in
        IEEE Transactions on Circuits and Systems I: Regular Papers
        Published version
        https://doi.org/10.1109/TCSI.2021.3081150
        Other links
        http://hdl.handle.net/2299/24580
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