An Artificial Neural Network based on Izhikevich Neuron Model

被引:0
|
作者
Taherkhani, Katayoon [1 ]
Shoorehdeli, Mahdi Aliyari [1 ]
机构
[1] KN Toosi Univ Technol, Fac Elect & Comp Engn, Tehran, Iran
关键词
SPIKING NEURONS;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the recent years, artificial neural network have been used to improvement of system identification. The performance of neural network directly depends on the hidden layer, which include weights and activation functions of the network. In addition Genetic Algorithms are used to learn of neural network as a type of evolutionary computing algorithms. In this paper, the structure of hidden layers and weights are modified by using biological neuron model of Izhikevich. These two methods, Genetic Algorithms and biological model of neuron, merge together for designing a novel structure.
引用
收藏
页码:807 / 811
页数:5
相关论文
共 50 条
  • [1] A High -Performance Neuron for Artificial Neural Network based on Izhikevich model
    Sapounaki, Maria
    Kakarountas, Athanasios
    2019 IEEE 29TH INTERNATIONAL SYMPOSIUM ON POWER AND TIMING MODELING, OPTIMIZATION AND SIMULATION (PATMOS 2019), 2019, : 29 - 34
  • [2] Silicon neuron circuit based on the Izhikevich model
    Mizoguchi, Nobuyuki
    Kohno, Takashi
    NEUROSCIENCE RESEARCH, 2011, 71 : E78 - E78
  • [3] Multiplicative neuron model artificial neural network based on Gaussian activation function
    Gundogdu, Ozge
    Egrioglu, Erol
    Aladag, Cagdas Hakan
    Yolcu, Ufuk
    NEURAL COMPUTING & APPLICATIONS, 2016, 27 (04): : 927 - 935
  • [4] Multiplicative neuron model artificial neural network based on Gaussian activation function
    Ozge Gundogdu
    Erol Egrioglu
    Cagdas Hakan Aladag
    Ufuk Yolcu
    Neural Computing and Applications, 2016, 27 : 927 - 935
  • [5] A Subthreshold Spiking Neuron Circuit Based on the Izhikevich Model
    Sato, Shigeo
    Moriya, Satoshi
    Kanke, Yuka
    Yamamoto, Hideaki
    Horio, Yoshihiko
    Yuminaka, Yasushi
    Madrenas, Jordi
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2021, PT V, 2021, 12895 : 177 - 181
  • [6] Bootstrapped Dendritic Neuron Model Artificial Neural Network for Forecasting
    Elif Olmez
    Erol Egrioglu
    Eren Bas
    Granular Computing, 2023, 8 : 1689 - 1699
  • [7] Bootstrapped Dendritic Neuron Model Artificial Neural Network for Forecasting
    Olmez, Elif
    Egrioglu, Erol
    Bas, Eren
    GRANULAR COMPUTING, 2023, 8 (06) : 1689 - 1699
  • [8] Spiking Neural Network Model MATLAB Implementation Based on Izhikevich Mathematical Model for Control Systems
    Sayarkin, Konstantin S.
    Popov, Alexey V.
    Zhilenkov, Anton A.
    PROCEEDINGS OF THE 2018 IEEE CONFERENCE OF RUSSIAN YOUNG RESEARCHERS IN ELECTRICAL AND ELECTRONIC ENGINEERING (EICONRUS), 2018, : 979 - 982
  • [9] A two-variable silicon neuron circuit based on the Izhikevich model
    Mizoguchi, Nobuyuki
    Nagamatsu, Yuji
    Aihara, Kazuyuki
    Kohno, Takashi
    PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS (AROB 16TH '11), 2011, : 338 - 341
  • [10] A two-variable silicon neuron circuit based on the Izhikevich model
    Mizoguchi, Nobuyuki
    Nagamatsu, Yuji
    Aihara, Kazuyuki
    Kohno, Takashi
    ARTIFICIAL LIFE AND ROBOTICS, 2011, 16 (03) : 383 - 388