Skip to main content
SpringerLink
Log in

Pulse-type hardware neural network mimicking spinal cord function

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

There is a lot of research on the application of artificial neural networks as a control method for robots to achieve bio-mimetic movements. In this paper, we have designed, fabricated, and measured a pulse-type hardware neural network that mimics spinal cord function. The model was able to change the pulse sequence, pulse width, and pulse frequency according to the input from a sensing model fabricated to mimic the pressure sensation of an organism. As a result, we have succeeded in developing an IC that can perform the same processing as the biological spinal cord. This makes it possible for a quadruped robot to not only change its gait but also to avoid danger by autonomously stopping its walking motion temporarily. The results show that this research has the potential to contribute to the realization of small robots with sophisticated and delicate movements, like living organisms.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Kimura H (2001) Introduction to research on biological mobile robots using neural models (in Japanese). Jpn Neural Netw Soc 8(2):70–76

    Google Scholar 

  2. Grillner S (1981) Control of locomotion in bipeds, tetrapods and fish. In: Terjung R (ed) Handbook of physiology, vol II. American Physiological Society, Bethesda, pp 1179–1236

    Google Scholar 

  3. Ito K (1993) Gait movement and rhythm generation (in Japanese). J Robot Soc Jpn 11(3):320–325

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Saeki K, and Sekine Y (1997) Pulse-type hardware neuron model with bursting firing phenomena. International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) pp 435–438

  6. Saeki K, Iidaka R, Sekine Y, Aihara K (2002) Hardware neuron models with CMOS for auditory neural networks. In: Proceedings of the 9th International Conference on Neural Information Processing, (ICONIP '02.) 3:1325–1329

  7. Tanaka D, Uchiumi Y, Kawamura S, Takato M, Saito K, Uchikoba F (2017) Four-leg independent mechanism for MEMS microrobot. Artif Life Robot (AROB) 22(3):380–384

    Article  Google Scholar 

  8. Kimura H, Akiyama S, Sakurama K (1999) Realization of dynamic walking and running of the quadruped using neural oscillator (in Japanese). Auton Robot 7:247–258

    Article  Google Scholar 

  9. Saeki K, Tatebe T, Sekine Y (2012) A study on CPG model transition swing and stance pattern with interstitial cells. The 2012 International Joint Conference on Neural Networks (IJCNN)

  10. Saeki K, Nihei D, Tatebe T, Sekine Y (2014) IC implementation of an interstitial cell-based CPG model. Analog Integr Circuit Signal Process 81:551–559

    Article  Google Scholar 

  11. Hanba M (1973) Origin of small and short action potential elicited during the relative refractory period of nerve, (in Japanese). KITAKANTO Med J 23(6):407–419

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by Nihon University Robotics Society (NUROS) and supported by Nihon University Multidisciplinary Research Grant for (2020). The authors appreciate the support. In addition, this work is supported by VLSI Design and Education Center (VDEC), the University of Tokyo in collaboration with Synopsys, Inc., Cadence Design Systems, Inc. and Mentor Graphics, Inc. The VLSI chip (Fig. 5) in this study has been fabricated in the chip fabrication program of VLSI Design and Education Center (VDEC), the University of Tokyo in collaboration with On-Semiconductor Niigata, and Toppan Printing Corporation. We would like to express my deepest gratitude here.

Author information

Authors and Affiliations

  1. Graduate School of Science and Technology, Nihon University, Funabashi , Japan

    Mikihito Hayakawa, Kenji Takeda & Motokuni Ishibashi

  2. College of Science and Technology, Nihon University, Funabashi , Japan

    Kaito Tanami, Megumi Aibara, Minami Kaneko, Ken Saito & Fumio Uchikoba

Authors
  1. Mikihito Hayakawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kenji Takeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Motokuni Ishibashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kaito Tanami
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Megumi Aibara
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Minami Kaneko
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ken Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fumio Uchikoba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fumio Uchikoba.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was presented in part at the 26th International Symposium on Artificial Life and Robotics (Online, January 21-23, 2021).

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hayakawa, M., Takeda, K., Ishibashi, M. et al. Pulse-type hardware neural network mimicking spinal cord function. Artif Life Robotics 26, 450–456 (2021). https://doi.org/10.1007/s10015-021-00691-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-021-00691-1

Keywords

Search

Navigation