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Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics

Author

Listed:
  • Pietro Belleri

    (University of Brescia)

  • Judith Pons i Tarrés

    (Max Planck Institute for Polymer Research)

  • Iain McCulloch

    (University of Oxford)

  • Paul W. M. Blom

    (Max Planck Institute for Polymer Research)

  • Zsolt M. Kovács-Vajna

    (University of Brescia)

  • Paschalis Gkoupidenis

    (Max Planck Institute for Polymer Research
    North Carolina State University
    North Carolina State University)

  • Fabrizio Torricelli

    (University of Brescia)

Abstract

Organic artificial neurons operating in liquid environments are crucial components in neuromorphic bioelectronics. However, the current understanding of these neurons is limited, hindering their rational design and development for realistic neuronal emulation in biological settings. Here we combine experiments, numerical non-linear simulations, and analytical tools to unravel the operation of organic artificial neurons. This comprehensive approach elucidates a broad spectrum of biorealistic behaviors, including firing properties, excitability, wetware operation, and biohybrid integration. The non-linear simulations are grounded in a physics-based framework, accounting for ion type and ion concentration in the electrolytic medium, organic mixed ionic-electronic parameters, and biomembrane features. The derived analytical expressions link the neurons spiking features with material and physical parameters, bridging closer the domains of artificial neurons and neuroscience. This work provides streamlined and transferable guidelines for the design, development, engineering, and optimization of organic artificial neurons, advancing next generation neuronal networks, neuromorphic electronics, and bioelectronics.

Suggested Citation

  • Pietro Belleri & Judith Pons i Tarrés & Iain McCulloch & Paul W. M. Blom & Zsolt M. Kovács-Vajna & Paschalis Gkoupidenis & Fabrizio Torricelli, 2024. "Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49668-1
    DOI: 10.1038/s41467-024-49668-1
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    References listed on IDEAS

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