An ion-electronic hybrid artificial neuron with a widely tunable frequency

Jidong Li, Wei Zhao, Chenwei Fu, Zhenpeng Zhai, Pengfei Xu, Xinyuan Diao, Wanlin Guo and Jun Yin ()
Additional contact information
Jidong Li: Nanjing University of Aeronautics and Astronautics
Wei Zhao: Nanjing University of Aeronautics and Astronautics
Chenwei Fu: Nanjing University of Aeronautics and Astronautics
Zhenpeng Zhai: Nanjing University of Aeronautics and Astronautics
Pengfei Xu: Nanjing University of Aeronautics and Astronautics
Xinyuan Diao: Nanjing University of Aeronautics and Astronautics
Wanlin Guo: Nanjing University of Aeronautics and Astronautics
Jun Yin: Nanjing University of Aeronautics and Astronautics

Nature Communications, 2025, vol. 16, issue 1, 1-8

Abstract: Abstract Biological nervous systems rely on distinct spiking frequencies across a wide range for perceiving, transmitting, processing, and executing information. Replicating this frequency range in an artificial neuron would facilitate the emulation of biosignal diversity but it remains challenging. Here, we develop an ion-electronic hybrid artificial neuron by compactly integrating a nonlinear electrochemical element with a solid-state memristor. This hybrid neuron employing a minimalist architecture exhibits a tunable spiking frequency spanning five orders of magnitude, significantly surpassing the capability of artificial neurons based on electronic devices. Notably, stimuli-dependent ion fluxes enable inherent afferent sensing of liquid flow, temperature, and chemical constituents, eliminating the need for separate, bulky sensors. Connection to biomotor nerves facilitates muscle actuation with frequency-regulated modes. The frequency encoding of a hybrid neuron array allows for the recognition of handwritten patterns. This hybrid neuron design, taking advantage of both ionic and electronic features, offers a promising approach for advanced e-skin and neurointerface technologies.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-63195-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63195-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-63195-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().