Evidence map›Paper›PMID 40855073›Full record

ArticleNature communications2025

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, Jun Yin

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jidong LiState Key Laboratory of Mechanics and Control for Aerospace Structures, Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.ORCID http://orcid.org/0000-0003-3955-5508
Wei ZhaoState Key Laboratory of Mechanics and Control for Aerospace Structures, Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
Chenwei FuState Key Laboratory of Mechanics and Control for Aerospace Structures, Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
Zhenpeng ZhaiState Key Laboratory of Mechanics and Control for Aerospace Structures, Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
Pengfei XuCollege of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
Xinyuan DiaoState Key Laboratory of Mechanics and Control for Aerospace Structures, Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
Wanlin GuoState Key Laboratory of Mechanics and Control for Aerospace Structures, Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.ORCID http://orcid.org/0000-0002-2302-8044
Jun YinState Key Laboratory of Mechanics and Control for Aerospace Structures, Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China. yinjun@nuaa.edu.cn.ORCID http://orcid.org/0000-0003-3375-8160

Funding

National Natural Science Foundation of China (National Science Foundation of China) 12172176National Natural Science Foundation of China (National Science Foundation of China) 12311530052National Natural Science Foundation of China (National Science Foundation of China) 12372329National Natural Science Foundation of China (National Science Foundation of China) T2293694
6 · The paper itself

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.

Indexed as

ElectronicsNeural Networks, ComputerNeuronsAction PotentialsIonsIons

Identifiers

PMID40855073
PMCPMC12379150

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.