The Journal of the Convergence on Culture Technology (문화기술의 융합)

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
권호 표지
DOI QR코드

DOI QR Code

An analysis of learning performance changes in spiking neural networks(SNN)

Spiking Neural Networks(SNN) 구조에서 뉴런의 개수와 학습량에 따른 학습 성능 변화 분석

  • Kim, Yongjoo (Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute (ETRI)) ;
  • Kim, Taeho
  • 김용주 (한국전자통신연구원 인공지능연구소) ;
  • 김태호 (한국전자통신연구원 인공지능연구소)
  • Received : 2020.04.18
  • Accepted : 2020.08.15
  • Published : 2020.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

Artificial intelligence researches are being applied and developed in various fields. In this paper, we build a neural network by using the method of implementing artificial intelligence in the form of spiking natural networks (SNN), the next-generation of artificial intelligence research, and analyze how the number of neurons in that neural networks affect the performance of the neural networks. We also analyze how the performance of neural networks changes while increasing the amount of neural network learning. The findings will help optimize SNN-based neural networks used in each field.

인공지능 연구는 다양한 분야에 적용되며 발전하고 있다. 본 논문에서는 차세대 인공지능 연구 분야인 SNN(Spiking Neural Networks) 형태의 인공지능 구현 방식을 사용하여 신경망을 구축하고, 그 신경망에서 뉴런의 개수가 신경망의 성능에 어떠한 영향을 미치는지를 분석한다. 또한 신경망 학습량을 증가시키면서 신경망의 성능이 어떻게 바뀌는지를 분석한다. 해당 연구 결과를 통해 각 분야에서 사용되는 SNN 기반의 신경망을 최적화 할 수 있을 것이다.

Keywords

References

  1. Hinton, Geoffrey E. "Learning multiple layers of representation". Trends in Cognitive Sciences. 11 (10): 428-434 https://doi.org/10.1016/j.tics.2007.09.004
  2. Hinton, G. E. et al. "A Fast Learning Algorithm for Deep Belief Nets" Neural Computation. 18 (7): 1527-1554 https://doi.org/10.1162/neco.2006.18.7.1527
  3. Alex Krizhevsky et al. "ImageNet Classification with Deep Convolutional Neural Networks" Communications of the ACM Vol. 60, No. 6
  4. Jaebok Park, Seungmok Yoo, Seokjin Yoon, Kyunghee Lee, Changsik Cho, "Interworking technology of neural network and data among deep learning frameworks" ETRI Journal Volume 41, Issue 6
  5. Abbott, L.F. "Lapique's introduction of the integrate-and-fire model neuron (1907)" Brain Research Bulletin. 50 (5/6): 303-304 https://doi.org/10.1016/S0361-9230(99)00161-6
  6. Bi, G. and Poo, M., "Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type," Journal of Neuroscience, vol. 18, no. 24, pp. 10464-10472, 1998. https://doi.org/10.1523/jneurosci.18-24-10464.1998
  7. P. U. Diehl, M. Cook, "Unsupervised learning of digit recognition using spike-timing-dependent plasticity," Frontiers in Computational Neuroscience, Aug. 2015.
  8. Hazan H., Saunders D., Sanghavi D., Siegelmann H., and Kozma, R., "Unsupervised learning with self-organizing spiking neural networks," in 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, Jul. 2018
  9. Matthias Oster and Shih-Chii Liu. 2006. Spiking inputs to a winner-take-all network. In Advances in Neural Information Processing Systems (NIPS). 1051-1058, 2004.
  10. Y. LeCun, L. Bottou, Y. Bengio, and P., Haffner, "Gradient-based learning applied to document recognition." Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998. https://doi.org/10.1109/5.726791
  11. D. F. M. Goodman, R. Brette, "The Brian simulator," Frontiers in Computational Neuroscience, Sep. 2009.
  12. Jee-Young Lee, "Deep Learning Research Trend Analysis using Text Mining" IJACT Vol.7 No.4
  13. Ju-eun Kim., "An Analysis of the effect of Artificial Intelligence on Human Society" JCCT Vol.5 No.2