References
[1]. Maass W. Networks of spiking neurons: The third generation of neural network models. Neural Networks. 1997;10(9):1659–1671.
[2]. Diehl, P. U., and Cook, M. (2015). Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9:99. doi:10.3389/fncom.2015.00099
[3]. G. Li, L. Deng, Y. Chua, P. Li, E. O. Neftci, and H. Li, “Spiking neural network learning, benchmarking, programming and executing,” Frontiers in Neuroscience, vol. 14, 2020.
[4]. Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. “Training Spiking Neural Networks Using Lessons From Deep Learning”. arXiv preprint arXiv:2109.12894, September 2021.
[5]. E. M. Izhikevich, “Which model to use for cortical spiking neurons?” IEEE transactions on neural networks, vol. 15, no. 5, pp. 1063–1070,2004.
[6]. A. M. Turing, “Computing machinery and intelligence,” Mind, vol. 59, no. 236, pp. 433–460, 1950.
[7]. E. M. Izhikevich, “Simple model of spiking neurons,” IEEE Transactions on neural networks, vol. 14, no. 6, pp. 1569–1572, 2003.
[8]. Anthony N Burkitt. 2006. A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biological cybernetics 95, 1 (2006), 1–19.
[9]. Diehl, P. U., Neil, D., Binas, J., Cook, M., Liu, S.-C., and Pfeiffer, M. (2015). “Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing,” in International Joint Conference on Neural Networks (IJCNN) (Anchorag, AK), 1–8. doi: 10.1109/ijcnn.2015.7280696
[10]. Indiveri, G., Corradi, F., and Qiao, N. (2015). “Neuromorphic architectures for spiking deep neural networks,” in 2015 IEEE International Electron Devices Meeting (IEDM) (Washington, DC: IEEE), 1–4. doi: 10.1109/iedm.2015.7409623
[11]. Sengupta, A., Ye, Y., Wang, R., Liu, C., & Roy, K. (2018). Going deeper in spiking networks: VGG and residual architectures, arXiv [Preprint]. arXiv:1802.02627v3.
[12]. Bi G-Q, Poo M-M. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 1998;18(24):10464.
Cite this article
Liu,Z. (2024). Research on handwritten digits recognition system based on spiking neuron network. Applied and Computational Engineering,30,284-291.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Maass W. Networks of spiking neurons: The third generation of neural network models. Neural Networks. 1997;10(9):1659–1671.
[2]. Diehl, P. U., and Cook, M. (2015). Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9:99. doi:10.3389/fncom.2015.00099
[3]. G. Li, L. Deng, Y. Chua, P. Li, E. O. Neftci, and H. Li, “Spiking neural network learning, benchmarking, programming and executing,” Frontiers in Neuroscience, vol. 14, 2020.
[4]. Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. “Training Spiking Neural Networks Using Lessons From Deep Learning”. arXiv preprint arXiv:2109.12894, September 2021.
[5]. E. M. Izhikevich, “Which model to use for cortical spiking neurons?” IEEE transactions on neural networks, vol. 15, no. 5, pp. 1063–1070,2004.
[6]. A. M. Turing, “Computing machinery and intelligence,” Mind, vol. 59, no. 236, pp. 433–460, 1950.
[7]. E. M. Izhikevich, “Simple model of spiking neurons,” IEEE Transactions on neural networks, vol. 14, no. 6, pp. 1569–1572, 2003.
[8]. Anthony N Burkitt. 2006. A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biological cybernetics 95, 1 (2006), 1–19.
[9]. Diehl, P. U., Neil, D., Binas, J., Cook, M., Liu, S.-C., and Pfeiffer, M. (2015). “Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing,” in International Joint Conference on Neural Networks (IJCNN) (Anchorag, AK), 1–8. doi: 10.1109/ijcnn.2015.7280696
[10]. Indiveri, G., Corradi, F., and Qiao, N. (2015). “Neuromorphic architectures for spiking deep neural networks,” in 2015 IEEE International Electron Devices Meeting (IEDM) (Washington, DC: IEEE), 1–4. doi: 10.1109/iedm.2015.7409623
[11]. Sengupta, A., Ye, Y., Wang, R., Liu, C., & Roy, K. (2018). Going deeper in spiking networks: VGG and residual architectures, arXiv [Preprint]. arXiv:1802.02627v3.
[12]. Bi G-Q, Poo M-M. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 1998;18(24):10464.