Ephaptic Coupling in Ultralow‐Power Ion‐Gel Nanofiber Artificial Synapses for Enhanced Working Memory

Y Yuanxia Chen J Junfeng Xia (Department of Biomedical Engineering Guangdong Provincial Key Laboratory of Advanced Biomaterials Institute of Innovative Materials Southern University of Science and Technology Shenzhen 518055 P.R. China) Y Youzhi Qu (Department of Biomedical Engineering Guangdong Provincial Key Laboratory of Advanced Biomaterials Institute of Innovative Materials Southern University of Science and Technology Shenzhen 518055 P.R. China) H Hongjie Zhang (State Key Laboratory of Rare Earths) T Tingting Mei X Xinyi Zhu (Department of Ultrasound, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital) G Guoheng Xu D Dongyang Li (Department of Materials Science and Engineering) L Li Wang (The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital Zhengzhou China) Q Quanying Liu K Kai Xiao

Abstract

AbstractNeuromorphic devices are designed to replicate the energy‐efficient information processing advantages found in biological neural networks by emulating the working mechanisms of neurons and synapses. However, most existing neuromorphic devices focus primarily on functionally mimicking biological synapses, with insufficient emphasis on ion transport mechanisms. This limitation makes it challenging to achieve the complexity and connectivity inherent in biological systems, such as ephaptic coupling. Here, an ionic biomimetic synaptic device based on a flexible ion‐gel nanofiber network is proposed, which transmits information and enables ephaptic coupling through capacitance formation by ion transport with an extremely low energy consumption of just 6 femtojoules. The hysteretic ion transport behavior endows the device with synaptic‐like memory effects, significantly enhancing the performance of the reservoir computing system for classifying the MNIST handwritten digit dataset and demonstrating high efficiency in edge learning. More importantly, the devices in an array establish communication connections, exhibiting global oscillatory behaviors similar to ephaptic coupling in biological neural networks. This connectivity enables the array to perform working memory tasks, paving the way for developing brain‐like systems characterized by high complexity and vast connectivity.

Article Details

Volume / Issue Vol. 37, Issue 16
Published April 01, 2025
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (11)

Y

Yuanxia Chen

J

Junfeng Xia

Department of Biomedical Engineering Guangdong Provincial Key Laboratory of Advanced Biomaterials Institute of Innovative Materials Southern University of Science and Technology Shenzhen 518055 P.R. China

Y

Youzhi Qu

Department of Biomedical Engineering Guangdong Provincial Key Laboratory of Advanced Biomaterials Institute of Innovative Materials Southern University of Science and Technology Shenzhen 518055 P.R. China

H

Hongjie Zhang

State Key Laboratory of Rare Earths

T

Tingting Mei

X

Xinyi Zhu

Department of Ultrasound, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital

G

Guoheng Xu

D

Dongyang Li

Department of Materials Science and Engineering

L

Li Wang

The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital Zhengzhou China

Q

Quanying Liu

K

Kai Xiao