Skip to main content

Advertisement

Springer Nature Link
Log in

Biomimetic snake locomotion using central pattern generators network and bio-hybrid robot perspective

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

Abstract

Neurological disorders affect millions globally and necessitate advanced treatments, especially with an aging population. Brain Machine Interfaces (BMIs) and neuroprostheses show promise in addressing disabilities by mimicking biological dynamics through biomimetic Spiking Neural Networks (SNNs). Central Pattern Generators (CPGs) are small neural networks that, emulated through biomimetic networks, can replicate specific locomotion patterns. Our proposal involves a real-time implementation of a biomimetic SNN on FPGA, utilizing biomimetic models for neurons, synaptic receptors and synaptic plasticity. The system, integrated into a snake-like mobile robot where the neuronal activity is responsible for its locomotion, offers a versatile platform to study spinal cord injuries. Lastly, we present a preliminary closed-loop experiment involving bidirectional interaction between the artificial neural network and biological neuronal cells, paving the way for bio-hybrid robots and insights into neural population functioning.

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

Access this article

Log in via an institution

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Brown T (1914) On the nature of the fundamental activity of the nervous centres; together with an analysis of the conditioning of rhythmic activity in progression and a theory of the evolution of function in the nervous system. J Physiol 48:18–46

    Article  Google Scholar 

  2. Cohen AH et al (1992) Modelling of intersegmental coordination in the lamprey central pattern generator for locomotion. Trends Neurosci 15:434–438

    Article  Google Scholar 

  3. Marder E, Bucher D (2001) Central pattern generators and the control of rhythmic movements. Curr Biol 11:986–996

    Article  Google Scholar 

  4. Ijspeert A et al (2007) From swimming to walking with a salamander robot driven by a spinal cord model. Science 315:1416–1420

    Article  Google Scholar 

  5. Hill AA, Calabrese RL et al (2001) A model of a segmental oscillator in the leech heartbeat neuronal network. J Comput Neurosci 10:281–302

    Article  Google Scholar 

  6. Cymbalyuk GS et al (2002) Cell-autonomous and network-based mechanisms bursting in leech heart interneurons. J Neurosci 22:10580–10592

    Article  Google Scholar 

  7. Matsuo T et al (2008) Biomimetic motion control system based on a CPG for an amphibious multi-link mobile robot. J Bionic Eng 8:91–97

    Article  Google Scholar 

  8. Donati E et al (2014) A spiking implementation of the lamprey’s Central Pattern Generator in neuromorphic VLSI. IEEE BioCAS 2014

  9. Van Der Pol B (1928) The heartbeat considered as a relaxation oscillation, and an electrical model of the heart. Phil Mag 6:763–775

    Article  Google Scholar 

  10. Amari S (1972) Characteristic of the random nets of analog neuron-like elements. IEEE Trans Syst Man Cybern 2:643–657

    Google Scholar 

  11. Ijspeert AJ (2008) Central pattern generators for locomotion control in animals and robots: a review. Neural Netw 21:642–653

    Article  Google Scholar 

  12. Li C, Lowe R, Ziemke T (2013) Humanoids learning to walk: a natural CPG-actor-critic architecture. Front Neurorobot 7:5

    Article  Google Scholar 

  13. Barron-Zambrano JH, Torres-Huitzil C (2013) FPGA implementation of a configurable neuromorphic CPG-based locomotion controller. Neural Netw 45:50–61

    Article  Google Scholar 

  14. Yu J, Tan M, Chen J, Zhang J (2014) A survey on CPG-inspired control models and system implementation. IEEE Trans Neural Netw Learn Syst 25(3):441–456

    Article  Google Scholar 

  15. Qi C et al (2017) A real-time FPGA implementation of a biologically inspired central pattern generator network. Neurocomputing 244:63–80

    Article  Google Scholar 

  16. Izhikevich EM (2004) Which model to use for cortical spiking neurons? IEEE Trans Neural Netw 15(5):1063–1070

    Article  Google Scholar 

  17. Bisio M et al (2019) Closed-loop systems and in vitro neuronal cultures: overview and applications. Adv Neurobiol 22:351–387

    Article  Google Scholar 

  18. Chiappalone M et al (2022) Neuromorphic-based neuroprostheses for brain rewiring: state-of-the-art and perspectives in neuroengineering. Brain Sci 2:8

    Google Scholar 

  19. Rowald A et al (2022) Activity-dependent spinal cord neuromodulation rapidly restores trunk and leg motor functions after complete paralysis. Nat Med 28:260–271

    Article  Google Scholar 

  20. Kathe C et al (2022) The neurons that restore walking after paralysis. Nature 611:540–547

    Article  Google Scholar 

  21. Beaubois R et al (2024) BiœmuS: a new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network. Nat Commun 15:5142

    Article  Google Scholar 

  22. Levi T et al (2018) Study of real-time biomimetic CPG on FPGA: behavior and evolution, International Conference on Artificial Life and Robotics (ICAROB 2018). ALife Robotics Corporation Ltd

  23. Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14(6):1569–1572

    Article  MathSciNet  Google Scholar 

  24. Blanchard D, Kohno T, Levi T (2019) Snake robot controlled by biomimetic CPGs, In International Conference on Artificial Life and Robotics (ICAROB 2019. ALife Robotics Corporation Ltd, Oita

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

    Article  Google Scholar 

  26. Pospischil M et al (2008) Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons. Biol Cybern 99:427–441

    Article  MathSciNet  Google Scholar 

  27. Khoyratee F et al (2019) Optimized real-time biomimetic neural network on FPGA for bio-hybridization. Front Neurosci 2:13–377

    Google Scholar 

  28. Destexhe A et al (1998) Kinetic models of synaptic transmission. Methods Neuronal Model 2:1–25

    Google Scholar 

  29. Kawada J et al (2017) Generation of a motor nerve organoid with human stem cell-derived neurons. Stem Cell Rep 9:1441–1449

    Article  Google Scholar 

  30. Kirihara T et al (2019) A human induced pluripotent stem cell-derived tissue model of a cerebral tract connecting two cortical regions. Iscience 14:301–311

    Article  Google Scholar 

  31. Osaki T et al (2024) Complex activity and short-term plasticity of human cerebral organoids reciprocally connected with axons. Nat Commun 15(1):2945

    Article  MathSciNet  Google Scholar 

  32. Levi T et al (2018) Closed-loop systems for next-generation neuroprostheses. Front Neurosci 2:12–26

    Google Scholar 

  33. Buccelli S et al (2019) A neuromorphic prosthesis to restore communication in neuronal networks. IScience 2:402–414

    Article  Google Scholar 

  34. Grassia F et al (2018) Spike pattern recognition using artificial neuron and spike-timing-dependent plasticity implemented on a multi-core embedded platform. Artif Life Robot 23:200–204

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IMS Laboratory, UMR5218, University of Bordeaux, Talence, France

    Jérémy Cheslet, Romain Beaubois, Farad Khoyratee & Timothée Lévi

  2. Institute of Industrial Science (IIS), The University of Tokyo, Tokyo, Japan

    Jérémy Cheslet, Romain Beaubois, Tomoya Duenki, Farad Khoyratee, Takashi Kohno & Yoshiho Ikeuchi

  3. LIMMS/CNRS-IIS, UMI 2820, The University of Tokyo, Tokyo, Japan

    Jérémy Cheslet, Romain Beaubois, Tomoya Duenki, Farad Khoyratee, Takashi Kohno, Yoshiho Ikeuchi & Timothée Lévi

  4. Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan

    Tomoya Duenki & Yoshiho Ikeuchi

  5. Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan

    Tomoya Duenki & Yoshiho Ikeuchi

Authors
  1. Jérémy Cheslet
    View author publications

    Search author on:PubMed Google Scholar

  2. Romain Beaubois
    View author publications

    Search author on:PubMed Google Scholar

  3. Tomoya Duenki
    View author publications

    Search author on:PubMed Google Scholar

  4. Farad Khoyratee
    View author publications

    Search author on:PubMed Google Scholar

  5. Takashi Kohno
    View author publications

    Search author on:PubMed Google Scholar

  6. Yoshiho Ikeuchi
    View author publications

    Search author on:PubMed Google Scholar

  7. Timothée Lévi
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Jérémy Cheslet.

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 joint symposium of the 29th International Symposium on Artificial Life and Robotics, the 9th International Symposium on BioComplexity, and the 7th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Beppu, Oita and Online, January 24–26, 2024).

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheslet, J., Beaubois, R., Duenki, T. et al. Biomimetic snake locomotion using central pattern generators network and bio-hybrid robot perspective. Artif Life Robotics 29, 479–485 (2024). https://doi.org/10.1007/s10015-024-00969-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-024-00969-0

Keywords