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Programmable coupled oscillators for synchronized locomotion

Author

Listed:
  • Sourav Dutta

    (University of Notre Dame)

  • Abhinav Parihar

    (Georgia Institute of Technology)

  • Abhishek Khanna

    (University of Notre Dame)

  • Jorge Gomez

    (University of Notre Dame)

  • Wriddhi Chakraborty

    (University of Notre Dame)

  • Matthew Jerry

    (University of Notre Dame)

  • Benjamin Grisafe

    (University of Notre Dame)

  • Arijit Raychowdhury

    (Georgia Institute of Technology)

  • Suman Datta

    (University of Notre Dame)

Abstract

The striking similarity between biological locomotion gaits and the evolution of phase patterns in coupled oscillatory network can be traced to the role of central pattern generator located in the spinal cord. Bio-inspired robotics aim at harnessing this control approach for generation of rhythmic patterns for synchronized limb movement. Here, we utilize the phenomenon of synchronization and emergent spatiotemporal pattern from the interaction among coupled oscillators to generate a range of locomotion gait patterns. We experimentally demonstrate a central pattern generator network using capacitively coupled Vanadium Dioxide nano-oscillators. The coupled oscillators exhibit stable limit-cycle oscillations and tunable natural frequencies for real-time programmability of phase-pattern. The ultra-compact 1 Transistor-1 Resistor implementation of oscillator and bidirectional capacitive coupling allow small footprint area and low operating power. Compared to biomimetic CMOS based neuron and synapse models, our design simplifies on-chip implementation and real-time tunability by reducing the number of control parameters.

Suggested Citation

  • Sourav Dutta & Abhinav Parihar & Abhishek Khanna & Jorge Gomez & Wriddhi Chakraborty & Matthew Jerry & Benjamin Grisafe & Arijit Raychowdhury & Suman Datta, 2019. "Programmable coupled oscillators for synchronized locomotion," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11198-6
    DOI: 10.1038/s41467-019-11198-6
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    Cited by:

    1. Chang Liu & Pek Jun Tiw & Teng Zhang & Yanghao Wang & Lei Cai & Rui Yuan & Zelun Pan & Wenshuo Yue & Yaoyu Tao & Yuchao Yang, 2024. "VO2 memristor-based frequency converter with in-situ synthesize and mix for wireless internet-of-things," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Rui Yuan & Pek Jun Tiw & Lei Cai & Zhiyu Yang & Chang Liu & Teng Zhang & Chen Ge & Ru Huang & Yuchao Yang, 2023. "A neuromorphic physiological signal processing system based on VO2 memristor for next-generation human-machine interface," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Milad Shafiee & Guillaume Bellegarda & Auke Ijspeert, 2024. "Viability leads to the emergence of gait transitions in learning agile quadrupedal locomotion on challenging terrains," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    4. Sourav Dutta & Georgios Detorakis & Abhishek Khanna & Benjamin Grisafe & Emre Neftci & Suman Datta, 2022. "Neural sampling machine with stochastic synapse allows brain-like learning and inference," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    5. Rui Yuan & Qingxi Duan & Pek Jun Tiw & Ge Li & Zhuojian Xiao & Zhaokun Jing & Ke Yang & Chang Liu & Chen Ge & Ru Huang & Yuchao Yang, 2022. "A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    6. Ke Yang & Yanghao Wang & Pek Jun Tiw & Chaoming Wang & Xiaolong Zou & Rui Yuan & Chang Liu & Ge Li & Chen Ge & Si Wu & Teng Zhang & Ru Huang & Yuchao Yang, 2024. "High-order sensory processing nanocircuit based on coupled VO2 oscillators," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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