Research on handwritten digits recognition system based on spiking neuron network

Research Article
Open access

Research on handwritten digits recognition system based on spiking neuron network

Zhao Liu 1*
  • 1 China University of Mining & Technology    
  • *corresponding author chizkiyahuohayon@gmail.com
Published on 31 January 2024 | https://doi.org/10.54254/2755-2721/30/20230052
ACE Vol.30
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-285-5
ISBN (Online): 978-1-83558-286-2

Abstract

In the 21st century, deep learning has revolutionized the fields of machine learning and computer science, attaining high accuracy in tasks such as image recognition. More layers and more parameters are stuffed into the network to achieve higher performance, making the network extremely large. A new, radically different approach was proposed to complete the tasks, such as image recognition, using a spiking neural network(SNN). The spiking neural network is event-driven rather than data-driven, which makes it more physiologically realistic and uses a lot less power. This study reviews the development of spiking neural networks and their differences from non-spiking neural networks, as well as the different encoding methods, neuronal models and update rules that have an impact on the performance of the network. It can be concluded that though SNNs can hardly achieve the same accuracy as artificial neural networks(ANN), the gap is narrowing. More strategies from ANN such as back-propagation and convolutional layers have been applied to SNNs, making it more accurate, stable and comprehensive.

Keywords:

neuromorphic intelligence, spiking neuron network, artificial neuron network, LIF model, SDTP

Liu,Z. (2024). Research on handwritten digits recognition system based on spiking neuron network. Applied and Computational Engineering,30,284-291.
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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.


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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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN:978-1-83558-285-5(Print) / 978-1-83558-286-2(Online)
Editor:Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.30
ISSN:2755-2721(Print) / 2755-273X(Online)

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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.