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FPGA implementation of motifs-based neuronal network and synchronization analysis

Author

Listed:
  • Deng, Bin
  • Zhu, Zechen
  • Yang, Shuangming
  • Wei, Xile
  • Wang, Jiang
  • Yu, Haitao

Abstract

Motifs in complex networks play a crucial role in determining the brain functions. In this paper, 13 kinds of motifs are implemented with Field Programmable Gate Array (FPGA) to investigate the relationships between the networks properties and motifs properties. We use discretization method and pipelined architecture to construct various motifs with Hindmarsh–Rose (HR) neuron as the node model. We also build a small-world network based on these motifs and conduct the synchronization analysis of motifs as well as the constructed network. We find that the synchronization properties of motif determine that of motif-based small-world network, which demonstrates effectiveness of our proposed hardware simulation platform. By imitation of some vital nuclei in the brain to generate normal discharges, our proposed FPGA-based artificial neuronal networks have the potential to replace the injured nuclei to complete the brain function in the treatment of Parkinson’s disease and epilepsy.

Suggested Citation

  • Deng, Bin & Zhu, Zechen & Yang, Shuangming & Wei, Xile & Wang, Jiang & Yu, Haitao, 2016. "FPGA implementation of motifs-based neuronal network and synchronization analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 388-402.
    • Handle: RePEc:eee:phsmap:v:451:y:2016:i:c:p:388-402
    DOI: 10.1016/j.physa.2016.01.052
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    References listed on IDEAS

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    1. Yu, Haitao & Wang, Jiang & Liu, Chen & Deng, Bin & Wei, Xile, 2013. "Delay-induced synchronization transitions in small-world neuronal networks with hybrid electrical and chemical synapses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5473-5480.
    2. Rosário, R.S. & Cardoso, P.T. & Muñoz, M.A. & Montoya, P. & Miranda, J.G.V., 2015. "Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 439(C), pages 7-19.
    3. Zhang, Ting & Wang, Jiang & Fei, Xiangyang & Deng, Bin, 2007. "Synchronization of coupled FitzHugh–Nagumo systems via MIMO feedback linearization control," Chaos, Solitons & Fractals, Elsevier, vol. 33(1), pages 194-202.
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    5. Yu, Haitao & Wang, Jiang & Liu, Qiuxiang & Sun, Jianbing & Yu, Haifeng, 2013. "Delay-induced synchronization transitions in small-world neuronal networks with hybrid synapses," Chaos, Solitons & Fractals, Elsevier, vol. 48(C), pages 68-74.
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