An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing

Tanmoy Sarkar, Katharina Lieberth, Aristea Pavlou, Thomas Frank, Volker Mailaender, Iain McCulloch, Paul W. M. Blom, Fabrizio Torricelli, Paschalis Gkoupidenis

Research output: Contribution to journalArticlepeer-review

69 Scopus citations


The effective mimicry of neurons is key to the development of neuromorphic electronics. However, artificial neurons are not typically capable of operating in biological environments, which limits their ability to interface with biological components and to offer realistic neuronal emulation. Organic artificial neurons based on conventional circuit oscillators have been created, but they require many elements for their implementation. Here we report an organic artificial neuron that is based on a compact nonlinear electrochemical element. The artificial neuron can operate in a liquid and is sensitive to the concentration of biological species (such as dopamine or ions) in its surroundings. The system offers in situ operation and spiking behaviour in biologically relevant environments—including typical physiological and pathological concentration ranges (5–150 mM)—and with ion specificity. Small-amplitude (1–150 mV) electrochemical oscillations and noise in the electrolytic medium shape the neuronal dynamics, whereas changes in ionic (≥2% over the physiological baseline) and biomolecular (≥ 0.1 mM dopamine) concentrations modulate the neuronal excitability. We also create biohybrid interfaces in which an artificial neuron functions synergistically and in real time with epithelial cell biological membranes.
Original languageEnglish (US)
JournalNature Electronics
StatePublished - Nov 7 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-11-30
Acknowledgements: We acknowledge A. Steinmetz, A. Becker, I. Krauhausen, D. Koutsouras, H. Ling, C. Bauer and M. Beuchel from MPI for Polymer Research (MPIP) for their valuable assistance. We also acknowledge E. van Dormele from the TU Eindhoven, A. Ascoli and R. Tetzlaff from the TU Dresden and D. Khodagholy from Columbia University for their valuable feedback. This work was performed at the facilities of MPIP (cleanroom, device metrology, electronics and mechanical workshop), which are supported by the Max Planck Society. T.S., P.W.M.B. and P.G. acknowledge funding from the Carl Zeiss Foundation via the Emergent AI Center of JGU Mainz. Open access funding provided by Max Planck Society.


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