期刊
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
卷 68, 期 8, 页码 3397-3410出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSI.2021.3081150
关键词
Biological neural networks; Neurons; Synchronization; Integrated circuit modeling; Brain modeling; Couplings; Numerical models; Bursting firing; synchronization; neural network; bifurcation; circuit implementation
资金
- Major Research Project of the National Natural Science Foundation of China [91964108]
- National Natural Science Foundation of China [61971185]
- Open Fund Project of Key Laboratory in Hunan Universities [18K010]
This paper investigates neural bursting and synchronization by modeling two neural network models based on the Hopfield neural network, showing that these networks can generate rich dynamic behaviors. The synchronization dynamics of the coupling neural network can produce different types of synchronous behaviors depending on the synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization.
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.
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