Skip to main content
SpringerLink
Log in
Book cover

International Conference on Robot Intelligence Technology and Applications

RiTA 2018: Robot Intelligence Technology and Applications pp 199–206Cite as

Spike Encoding Modules Using Neuron Model in Neural Networks

  • Conference paper
  • First Online:

  • 726 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1015))

Abstract

There has been a great increase in performance of deep neural networks. However, for mobile devices which are not equipped with GPU (Graphics Processing Unit) or powerful CPU (Central Processing Unit), it is still impossible to deal with such a large amount of data in real time. In this paper, preliminary results in spike neural encoding methods reducing the amount of the input and computational load by mimicking the neuronal firing are presented. For this, two neuron models, leaky integrate-and-fire (LIF) model and simplified IF model, are exploited for transforming the input image to the spike image. For the evaluation, MNIST datasets are encoded and tested in deep neural networks for checking the loss of information. The proposed spike encoding modules using neuron models will be able to greatly help reduce required computation by using spike input data in low powered mobile devices.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Proceedings of Neural Information Processing Systems (NIPS), pp. 1097–1105 (2012)

    Google Scholar 

  2. Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)

    Article  Google Scholar 

  3. Mikolov, T., Karafiat, M., Burget, L., Cernocky, J., Khudanpur, S.: Recurrent neural network based language model. In: Interspeech, vol. 2, p. 3 (2010)

    Google Scholar 

  4. Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Proceedings of Neural Information Processing Systems (NIPS) (2015)

    Google Scholar 

  5. Tavanaei, A., Maida, A.: Bio-inspired spiking convolutional neural network using layer-wise sparse coding and STDP learning. arXiv preprint arXiv:1611.03000 (2016)

  6. Trabelsi, C., et al.: Deep complex networks. arXiv preprint arXiv:1705.09792 (2017)

  7. Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks: training deep neural networks with weights and activations constrained to +1 or −1. arXiv preprint arXiv:1602.02830 (2016)

  8. Long, L.: Scalable biologically inspired neural networks with spike time based learning. In: Proceedings Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008, pp. 29–34. ECSIS (2008)

    Google Scholar 

  9. Lee, S., Kim, K., Kim, J., Kim, Y., Myung, H.: Spike-inspired deep neural network design using binary weight. In: Proceedings of International Conference on Control, Automation and Systems (ICCAS) (2018)

    Google Scholar 

  10. Kim, K., Kim, J., Lee, S., Kim, Y., Myung, H.: Development of the image to spike conversion algorithm for the deep neural networks. In: Proceedings of International Conference on Control, Automation and Systems (ICCAS) (2018)

    Google Scholar 

  11. Abbott, L.F.: Lapicque’s introduction of the integrate-and-fire model neuron (1907). Brain Res. Bull. 50(5–6), 303–304 (1999)

    Article  Google Scholar 

  12. Xie, X., Qu, H., Yi, Z., Kurths, J.: Efficient training of supervised spiking neural network via accurate synaptic-efficiency adjustment method. IEEE Trans. Neural Netw. Learn. Syst. 28(6), 1411–14247 (2017)

    Article  Google Scholar 

  13. Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569–1572 (2013)

    Article  Google Scholar 

  14. Hodgkin, A., Huxley, A.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117(4), 500–544 (1952)

    Article  Google Scholar 

  15. Hodgkin, A., Huxley, A., Katz, B.: Measurement of current-voltage relations in the membrane of the giant axon of Loligo. J. Physiol. 116(4), 424–448 (1952)

    Article  Google Scholar 

  16. Hodgkin, A., Huxley, A.: Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J. Physiol. 116(4), 449–472 (1952)

    Article  Google Scholar 

  17. Hodgkin, A., Huxley, A.: The components of membrane conductance in the giant axon of Loligo. J. Physiol. 116(4), 473–496 (1952)

    Article  Google Scholar 

  18. Runge, C.: Über die numerische Auflösung von Differentialgleichungen. Mathematische Annalen 46(2), 167–178 (1895). Springer

    Article  MathSciNet  Google Scholar 

  19. LeCun, Y., Cortes, C., Burges, C.: MNIST handwritten digit database. AT&T Labs, (2018). http://yann.lecun.com/exdb/mnist

  20. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS), pp. 1097–1105 (2012)

    Google Scholar 

  21. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015)

    Google Scholar 

  22. NVIDIA: DIGITS Deep Learning Framework (2018). https://github.com/NVIDIA/DIGITS

  23. Nemoto, I., Saito, K.: A complex-valued version of Nagumo-Sato model of a single neuron and its behavior. Neural Netw. 15(7), 833–853 (2002)

    Article  Google Scholar 

  24. Iakymchuk, T., Rosado-Muñoz, A., Guerrero-Martínez, J., Bataller-Mompeán, M., Francés-Víllora, J.: Simplified spiking neural network architecture and STDP learning algorithm applied to image classification. EURASIP J. Image Video Process. (2015)

    Google Scholar 

  25. Bologna, L., et al.: A closed-loop neurobotic system for fine touch sensing. J. Neural Eng. 10(4), 046019 (2016)

    Article  Google Scholar 

  26. Thorpe, S., Delorme, A., Van Rullen, R.: Spike-based strategies for rapid processing. Neural Netw. 14(6-7), 715–725 (2001)

    Article  Google Scholar 

  27. Liu, Q., et al.: Benchmarking spike-based visual recognition: a dataset and evaluation. Front. Neurosci. 10, 496 (2016)

    Google Scholar 

  28. Heeger, D.: Poisson model of spike generation. Handout Univ. Standford 5, 1–13 (2000)

    Google Scholar 

  29. Fatahi, M., et al.: evt_MNIST: a spike based version of traditional MNIST. arXiv preprint arXiv:1604.06751 (2016)

  30. Sen, B., Steve, F.: Evaluating rank-order code performance using a biologically-derived retinal model. In: International Joint Conference on IEEE (IJCNN), pp. 2867–2874 (2009)

    Google Scholar 

  31. Serrano-Gotarredona, T., Linares-Barranco, B.: A 128 × 128 1.5% contrast sensitivity 0.9% FPN 3 µs latency 4 mW asynchronous frame-free dynamic vision sensor using transimpedance preamplifiers. J. Solid-State Circ. 48(3), 827–838 (2013)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by the ICT R&D program of MSIP/IITP (2016-0-00563, Research on Adaptive Machine Learning Technology Development for Intelligent Autonomous Digital Companion). The students are supported by Korea Minister of Ministry of Land, Infrastructure and Transport (MOLIT) as U-City Master and Doctor Course Grant Program.

Author information

Authors and Affiliations

  1. Robotics Program, Korean Advanced Institute for Science and Technology (KAIST), Daejeon, 34141, Korea

    Yeeun Kim & Hyun Myung

  2. Department of Civil and Environmental Engineering, Korean Advanced Institute for Science and Technology (KAIST), Daejeon, 34141, Korea

    Seunghee Lee, Wonho Song & Hyun Myung

Authors
  1. Yeeun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seunghee Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wonho Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hyun Myung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yeeun Kim .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea (Republic of)

    Jong-Hwan Kim

  2. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea (Republic of)

    Hyung Myung

  3. Keimyung University, Daegu, Korea (Republic of)

    Seung-Mok Lee

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kim, Y., Lee, S., Song, W., Myung, H. (2019). Spike Encoding Modules Using Neuron Model in Neural Networks. In: Kim, JH., Myung, H., Lee, SM. (eds) Robot Intelligence Technology and Applications. RiTA 2018. Communications in Computer and Information Science, vol 1015. Springer, Singapore. https://doi.org/10.1007/978-981-13-7780-8_16

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-7780-8_16

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7779-2

  • Online ISBN: 978-981-13-7780-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation