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A low-power stretchable neuromorphic nerve with proprioceptive feedback

Nature Biomedical Engineering volume 7, pages 511–519 (2023)Cite this article

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An Author Correction to this article was published on 08 September 2022

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Abstract

By relaying neural signals from the motor cortex to muscles, devices for neurorehabilitation can enhance the movement of limbs in which nerves have been damaged as a consequence of injuries affecting the spinal cord or the lower motor neurons. However, conventional neuroprosthetic devices are rigid and power-hungry. Here we report a stretchable neuromorphic implant that restores coordinated and smooth motions in the legs of mice with neurological motor disorders, enabling the animals to kick a ball, walk or run. The neuromorphic implant acts as an artificial efferent nerve by generating electrophysiological signals from excitatory post-synaptic signals and by providing proprioceptive feedback. The device operates at low power (~1/150 that of a typical microprocessor system), and consists of hydrogel electrodes connected to a stretchable transistor incorporating an organic semiconducting nanowire (acting as an artificial synapse), connected via an ion gel to an artificial proprioceptor incorporating a carbon nanotube strain sensor (acting as an artificial muscle spindle). Stretchable electronics with proprioceptive feedback may inspire the further development of advanced neuromorphic devices for neurorehabilitation.

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Fig. 1: SNEN.
Fig. 2: Muscle contraction.
Fig. 3: Artificial proprioception.
Fig. 4: Bipedal walking locomotion.
Fig. 5: Electrophysiological signals.

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Data availability

The authors declare that all data supporting the findings of this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are available from the corresponding authors on request.

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Acknowledgements

This work was supported by the Creative-Pioneering Researchers Program through Seoul National University (SNU), the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (grant no. NRF-2016R1A3B1908431), and the Pioneer Research Center Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (grant no. NRF-2022M3C1A3081211). Y. Liu acknowledges National Science Scholarship (NSS) funding support from Agency for Science Technology and Research Singapore (A*STAR).

Author information

Author notes

  1. These authors contributed equally: Yeongjun Lee, Yuxin Liu, Dae-Gyo Seo.

Authors and Affiliations

  1. Department of Materials Science and Engineering, Seoul National University, Seoul, Republic of Korea

    Yeongjun Lee, Dae-Gyo Seo & Tae-Woo Lee

  2. Department of Chemical Engineering, Stanford University, Stanford, CA, USA

    Yeongjun Lee, Jin Young Oh, Jinxing Li, Jiheong Kang, Jaemin Kim, Jaewan Mun, Amir M. Foudeh & Zhenan Bao

  3. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Yuxin Liu

  4. Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore

    Yuxin Liu

  5. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

    Yeongin Kim

  6. School of Chemical and Biological Engineering, Seoul National University, Seoul, Republic of Korea

    Tae-Woo Lee

  7. Institute of Engineering Research, Research Institute of Advanced Materials, Soft Foundry, Seoul National University, Seoul, Republic of Korea

    Tae-Woo Lee

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Contributions

Y. Lee, Y. Liu, D.-G.S., Z.B. and T.-W.L. conceived of and designed the overall experiments. Y. Lee, Y. Liu and D.-G.S. conducted experiments and collected related data. J.Y.O. helped to fabricate synaptic transistors. Y.K. contributed to analysis of electrical circuit. J.L. helped with experiments on mice. J. Kang, J. Kim and J.M. contributed to strain sensor fabrication and measurements. A.M.F. aided in image visualization. Y. Lee, Y. Liu, D.-G.S., Z.B. and T.-W.L. analysed all data and co-wrote the paper. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Zhenan Bao or Tae-Woo Lee.

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Nature Biomedical Engineering thanks Silvestro Micera, Cunjiang Yu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Demonstration of practical locomotion of ‘kicking a ball’.

a, Design of the mouse for kicking a ball. An extensor was connected to SNEN system and the swing motion was controlled by synaptic signals. b,c, Photographs of the mouse kicking a ball with a weak and short muscle contraction (a small swing) (b) and a strong and long muscle contraction (a full swing) (c).

Extended Data Fig. 2 Signals of SNEN for bipedal walking locomotion.

a–d, Presynaptic input spike patterns (upper) and resultant EPSCs (lower) with different moving speeds of 0.8 cm/s (slow walking) (a), 1 cm/s (fast walking) (b), 1.6 cm/s (jogging) (c), and 2.5 cm/s (running) (d).

Extended Data Fig. 3 SNEN with electrophysiological signals with different firing rates.

a, b, Presynaptic input spike patterns referred from neural data (a) and resultant EPSCs (b) with high firing rate (34.8 Hz) (neuron 1, red) and low firing rate (2.8 Hz) (neuron 2, black).

Supplementary information

Supplementary Information

Supplementary figures, tables, notes, video captions and references.

Reporting Summary

Supplementary Video 1

Leg locomotion depending on AP firing frequency from 1 Hz to 11 Hz.

Supplementary Video 2

Synchronized movement of flexion and extension.

Supplementary Video 3

Kicking motions.

Supplementary Video 4

Bipedal walking locomotion.

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Lee, Y., Liu, Y., Seo, DG. et al. A low-power stretchable neuromorphic nerve with proprioceptive feedback. Nat. Biomed. Eng 7, 511–519 (2023). https://doi.org/10.1038/s41551-022-00918-x

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