Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A chemically mediated artificial neuron

An Author Correction to this article was published on 16 September 2022

This article has been updated

Abstract

Brain–machine interfaces typically rely on electrophysiological signals to interpret and transmit neurological information. In biological systems, however, neurotransmitters are chemical-based interneuron messengers. This mismatch can potentially lead to incorrect interpretation of the transmitted neuron information. Here we report a chemically mediated artificial neuron that can receive and release the neurotransmitter dopamine. The artificial neuron detects dopamine using a carbon-based electrochemical sensor and then processes the sensory signals using a memristor with synaptic plasticity, before stimulating dopamine release through a heat-responsive hydrogel. The system responds to dopamine exocytosis from rat pheochromocytoma cells and also releases dopamine to activate pheochromocytoma cells, forming a chemical communication loop similar to interneurons. To illustrate the potential of this approach, we show that the artificial neuron can trigger the controllable movement of a mouse leg and robotic hand.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Conceptual schematic of a neurotransmitter-mediated artificial neuron.
Fig. 2: Characterization of individual building blocks for artificial neuron.
Fig. 3: Chemically mediated artificial synapse with in-sensing memory and memristor-mediated DA-releasing behaviours.
Fig. 4: System demonstration of an interneuron.
Fig. 5: Artificial neuron for neurointerfaces.

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Change history

References

  1. Chaudhary, U., Birbaumer, N. & Ramos-Murguialday, A. Brain–computer interfaces for communication and rehabilitation. Nat. Rev. Neurol. 12, 513–525 (2016).

    Article  Google Scholar 

  2. Lee, Y. & Lee, T.-W. Organic synapses for neuromorphic electronics: from brain-inspired computing to sensorimotor nervetronics. Acc. Chem. Res. 52, 964–974 (2019).

    Article  Google Scholar 

  3. Simon, D. T., Gabrielsson, E. O., Tybrandt, K. & Berggren, M. Organic bioelectronics: bridging the signaling gap between biology and technology. Chem. Rev. 116, 13009–13041 (2016).

    Article  Google Scholar 

  4. Zhang, M., Tang, Z., Liu, X. & Van der Spiegel, J. Electronic neural interfaces. Nat. Electron. 3, 191–200 (2020).

    Article  Google Scholar 

  5. Patel, S. R. & Lieber, C. M. Precision electronic medicine in the brain. Nat. Biotechnol. 37, 1007–1012 (2019).

    Google Scholar 

  6. Araki, T. et al. Flexible neural interfaces for brain implants—the pursuit of thinness and high density. Flex. Print. Electron. 5, 043002 (2020).

    Article  Google Scholar 

  7. Liu, Y. et al. Soft conductive micropillar electrode arrays for biologically relevant electrophysiological recording. Proc. Natl Acad. Sci. USA 115, 11718–11723 (2018).

    Article  Google Scholar 

  8. Schiavone, G. et al. Bioelectronic interfaces: soft, implantable bioelectronic interfaces for translational research. Adv. Mater. 32, 2070133 (2020).

    Article  Google Scholar 

  9. Sunwoo, S.-H. et al. Advances in soft bioelectronics for brain research and clinical neuroengineering. Matter 3, 1923–1947 (2020).

    Article  Google Scholar 

  10. Sabandal, J. M., Berry, J. A. & Davis, R. L. Dopamine-based mechanism for transient forgetting. Nature 591, 426–430 (2021).

    Article  Google Scholar 

  11. Berke, J. D. What does dopamine mean? Nat. Neurosci. 21, 787–793 (2018).

    Article  Google Scholar 

  12. Pristerà, A. et al. Dopamine neuron-derived IGF-1 controls dopamine neuron firing, skill learning, and exploration. Proc. Natl Acad. Sci. USA 116, 3817–3826 (2019).

    Article  Google Scholar 

  13. Wang, T. et al. Cyber–physiochemical interfaces. Adv. Mater. 32, 1905522 (2020).

    Article  Google Scholar 

  14. Patriarchi, T. et al. Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors. Science 360, eaat4422 (2018).

    Article  Google Scholar 

  15. da Silva, J. A., Tecuapetla, F., Paixão, V. & Costa, R. M. Dopamine neuron activity before action initiation gates and invigorates future movements. Nature 554, 244–248 (2018).

    Article  Google Scholar 

  16. Grace, A. A. Dysregulation of the dopamine system in the pathophysiology of schizophrenia and depression. Nat. Rev. Neurosci. 17, 524–532 (2016).

    Article  Google Scholar 

  17. Song, K. M. et al. Skyrmion-based artificial synapses for neuromorphic computing. Nat. Electron. 3, 148–155 (2020).

    Article  Google Scholar 

  18. Zhang, X. et al. An artificial spiking afferent nerve based on Mott memristors for neurorobotics. Nat. Commun. 11, 51 (2020).

    Article  Google Scholar 

  19. Wu, Z. et al. A habituation sensory nervous system with memristors. Adv. Mater. 32, 2004398 (2020).

    Article  Google Scholar 

  20. Choi, C. et al. Curved neuromorphic image sensor array using a MoS2-organic heterostructure inspired by the human visual recognition system. Nat. Commun. 11, 5934 (2020).

    Article  Google Scholar 

  21. Wan, C. et al. Artificial sensory memory. Adv. Mater. 32, 1902434 (2020).

    Article  Google Scholar 

  22. Zhang, S. et al. Selective release of different neurotransmitters emulated by a p–i–n junction synaptic transistor for environment-responsive action control. Adv. Mater. 33, 2007350 (2021).

    Article  Google Scholar 

  23. Pickett, M. D., Medeiros-Ribeiro, G. & Williams, R. S. A scalable neuristor built with Mott memristors. Nat. Mater. 12, 114–117 (2013).

    Article  Google Scholar 

  24. Wan, C. et al. An artificial sensory neuron with visual-haptic fusion. Nat. Commun. 11, 4602 (2020).

    Article  Google Scholar 

  25. Tan, H. et al. Tactile sensory coding and learning with bio-inspired optoelectronic spiking afferent nerves. Nat. Commun. 11, 1369 (2020).

    Article  Google Scholar 

  26. Tuma, T. et al. Stochastic phase-change neurons. Nat. Nanotechnol. 11, 693–699 (2016).

    Article  Google Scholar 

  27. Kim, Y. et al. A bioinspired flexible organic artificial afferent nerve. Science 360, 998–1003 (2018).

    Article  Google Scholar 

  28. Yoon, J. H. et al. An artificial nociceptor based on a diffusive memristor. Nat. Commun. 9, 417 (2018).

    Article  Google Scholar 

  29. Lee, Y. et al. Stretchable organic optoelectronic sensorimotor synapse. Sci. Adv. 4, eaat7387 (2018).

    Article  Google Scholar 

  30. Keene, S. T. et al. A biohybrid synapse with neurotransmitter-mediated plasticity. Nat. Mater. 19, 969–973 (2020).

    Article  Google Scholar 

  31. Mohebi, A. et al. Dissociable dopamine dynamics for learning and motivation. Nature 570, 65–70 (2019).

    Article  Google Scholar 

  32. Lin, R. et al. The raphe dopamine system controls the expression of incentive memory. Neuron 106, 498–514.e8 (2020).

    Article  Google Scholar 

  33. Squair, J. W. et al. Neuroprosthetic baroreflex controls haemodynamics after spinal cord injury. Nature 590, 308–314 (2021).

    Article  Google Scholar 

  34. Wang, M. et al. Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors. Nat. Electron. 3, 563–570 (2020).

    Article  Google Scholar 

  35. Moon, J.-M. et al. Conducting polymer-based electrochemical biosensors for neurotransmitters: a review. Biosens. Bioelectron. 102, 540–552 (2018).

    Article  Google Scholar 

  36. Liu, X. & Liu, J. Biosensors and sensors for dopamine detection. VIEW 2, 20200102 (2021).

    Article  Google Scholar 

  37. Wang, Z. et al. Resistive switching materials for information processing. Nat. Rev. Mater. 5, 173–195 (2020).

    Article  Google Scholar 

  38. Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 1, 22–29 (2018).

    Article  Google Scholar 

  39. Oh, Y. et al. Tracking tonic dopamine levels in vivo using multiple cyclic square wave voltammetry. Biosens. Bioelectron. 121, 174–182 (2018).

    Article  Google Scholar 

  40. Ribeiro, J. A., Fernandes, P. M. V., Pereira, C. M. & Silva, F. Electrochemical sensors and biosensors for determination of catecholamine neurotransmitters: a review. Talanta 160, 653–679 (2016).

    Article  Google Scholar 

  41. Chen, G. et al. Plasticizing silk protein for on-skin stretchable electrodes. Adv. Mater. 30, 1800129 (2018).

    Article  Google Scholar 

  42. Wang, W. et al. Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices. Nat. Commun. 10, 81 (2019).

    Article  Google Scholar 

  43. Chae, B.-G. et al. Nanometer-scale phase transformation determines threshold and memory switching mechanism. Adv. Mater. 29, 1701752 (2017).

    Article  Google Scholar 

  44. Marten, F. L. Vinyl alcohol polymers. in Encyclopedia of Polymer Science and Technology (Wiley, 2002).

  45. Hassan, C. M. & Peppas, N. A. Structure and morphology of freeze/thawed PVA hydrogels. Macromolecules 33, 2472–2479 (2000).

    Article  Google Scholar 

  46. Lian, Z. & Ye, L. Effect of PEO on the network structure of PVA hydrogels prepared by freezing/thawing method. J. Appl. Polym. Sci. 128, 3325–3329 (2013).

    Article  Google Scholar 

  47. Lisman, J., Cooper, K., Sehgal, M. & Silva, A. J. Memory formation depends on both synapse-specific modifications of synaptic strength and cell-specific increases in excitability. Nat. Neurosci. 21, 309–314 (2018).

    Article  Google Scholar 

  48. Rashid, A. J. et al. D1–D2 dopamine receptor heterooligomers with unique pharmacology are coupled to rapid activation of Gq/11 in the striatum. Proc. Natl Acad. Sci. USA 104, 654–659 (2007).

    Article  Google Scholar 

  49. Liu, C., Goel, P. & Kaeser, P. S. Spatial and temporal scales of dopamine transmission. Nat. Rev. Neurosci. 22, 345–358 (2021).

    Article  Google Scholar 

  50. Li, J. et al. Construction of dopamine-releasing gold surfaces mimicking presynaptic membrane by on-chip electrochemistry. J. Am. Chem. Soc. 141, 8816–8824 (2019).

    Google Scholar 

  51. Wang, J. et al. Artificial sense technology: emulating and extending biological senses. ACS Nano 15, 18671–18678 (2021).

    Article  Google Scholar 

  52. Shin, M., Wang, Y., Borgus, J. R. & Venton, B. J. Electrochemistry at the synapse. Annu. Rev. Anal. Chem. 12, 297 (2019).

    Article  Google Scholar 

  53. Lee, M.-J. et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5–x/TaO2–x bilayer structures. Nat. Mater. 10, 625–630 (2011).

    Article  Google Scholar 

  54. Li, C. et al. Ultralow power switching of Ta2O5/AlOx bilayer synergistic resistive random access memory. J. Phys. D: Appl. Phys. 53, 335104 (2020).

    Article  Google Scholar 

  55. Zhao, C. et al. Implantable aptamer field-effect transistor neuroprobes for in vivo neurotransmitter monitoring. Sci. Adv. 7, eabj7422 (2021).

    Article  Google Scholar 

  56. Skrabalak, S. E., Au, L., Li, X. & Xia, Y. Facile synthesis of Ag nanocubes and Au nanocages. Nat. Protoc. 2, 2182–2190 (2007).

    Article  Google Scholar 

  57. Wang, T. et al. Mechanical tolerance of cascade bioreactions via adaptive curvature engineering for epidermal bioelectronics. Adv. Mater. 32, 2000991 (2020).

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge financial support from the National Key Research and Development Program of China (2017YFA0205302, L.W.); Natural Science Foundation of Jiangsu Province—Major Project (BK20212012, L.W.); National Key R&D Program of China (2021YFB3601200, M.W.); National Natural Science Foundation of China (81971701, B.H.); the Natural Science Foundation for Young Scholars of Jiangsu Province (BK20210596, T.W.); the Natural Science Foundation of Jiangsu Province (BK20201352, B.H.); the Program of Jiangsu Specially-Appointed Professor (B.H. and T.W.); Science Foundation of Nanjing University of Post and Telecommunications (NUPTSF, NY221004, T.W.); the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project #A18A1b0045, X.C.); the National Research Foundation (NRF), Prime Minister’s Office, Singapore, under its NRF Investigatorship (NRF-NRFI2017-07, X.C.); and Singapore Ministry of Education (MOE2017-T2-2-107, X.C.).

Author information

Authors and Affiliations

Authors

Contributions

T.W., M.W. and X.C. designed the study. T.W., J.W., Z.L., H.Z. and J.N. designed and characterized the DA sensor and hydrogel. T.W., X.Y., R.L., Y.T., K.C., B.Y., S.L. and B.H. characterized the artificial neuron/PC12 cells biointerface. S.L. and F.L conducted the patch-clamping test. S.C, Y.Y., B.H., G.X. and X.F. characterized the artificial neuron/sciatic biointerface. T.W. and M.W. designed and characterized the memristor. T.W., Y.L., S.J. and Z.C. fabricated the hydrogel and microfluidics. T.W. and M.W. fabricated and characterized the system. T.W., M.W., L.W. and X.C. wrote the paper and all the authors provided feedback.

Corresponding authors

Correspondence to Benhui Hu or Xiaodong Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Electronics thanks Jun Chen, Tsuyoshi Sekitani and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Notes 1–12, Table 1, Figs. 1–25 and refs. 1–12.

Reporting Summary

Supplementary Video 1

DA-modulated releasing behaviour.

Supplementary Video 2

Fluorescence Ca2+ imaging to indicate the activation of neuron cells by the released DA stimuli.

Supplementary Video 3

An afferent motor neuron where DA triggers muscle contraction.

Supplementary Data 1

Performance variations based on 12 sensors.

Supplementary Data 2

Device-to-device variation in the memory switching characteristics for ten memristor devices.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, T., Wang, M., Wang, J. et al. A chemically mediated artificial neuron. Nat Electron 5, 586–595 (2022). https://doi.org/10.1038/s41928-022-00803-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-022-00803-0

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing