Soft Iontronics for Neuromorphic Vision: From Biological Mechanisms to Bio‐Integrated Applications

ABSTRACT Artificial vision systems play an important role in machine vision and artificial visual prostheses. Drawing inspiration from biological neural systems, neuromorphic devices have emerged as a promising platform for bio‐inspired vision systems, providing unprecedented opportunities to address the limitations of traditional von Neumann architecture by integrating sensing, memory, and computation into a single unit, enabling high efficiency. Despite the fast growth of electronic neuromorphic devices, iontronic neuromorphic devices have demonstrated great potential in processing dynamic visual tasks. By emulating biological ion transport and synaptic signal transmission, they offer inherent biomimicry, low power consumption, and excellent compatibility with soft tissues. This review first analyzes the fundamental working mechanisms of biological vision systems, with a particular focus on ion‐mediated signal transduction across cellular membranes and synapses, which form the theoretical basis for ion‐based neuromorphic devices. We then delve into the working principles of ionic devices in simulating biological vision, theoretically demonstrating the feasibility of constructing neuromorphic visual systems using ionic devices. Next, the review highlights cutting‐edge applications in neuromorphic visual perception and bio‐integrated visual systems based on soft iontronics. By bridging material innovation and biomimetic engineering, soft iontronics provides a roadmap toward energy‐efficient, biocompatible vision systems that harmonize artificial and biological intelligence.

Authors

Institutions

Publication Details

Journal
Advanced Materials Technologies
Published
2025-11-23
DOI
https://doi.org/10.1002/admt.202501963
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Soft Iontronics for Neuromorphic Vision: From Biological Mechanisms to Bio‐Integrated Applications

Kai Xiao, Xiaolong Zhu, Junjun Liu, Ling Zheng
1 citations
Advanced Materials Technologies
Advanced Memory and Neural Computing
0.73
article

Soft Iontronics for Neuromorphic Vision: From Biological Mechanisms to Bio‐Integrated Applications

Kai Xiao, Xiaolong Zhu, Junjun Liu, Ling Zheng
article en
1 citations

Abstract

ABSTRACT Artificial vision systems play an important role in machine vision and artificial visual prostheses. Drawing inspiration from biological neural systems, neuromorphic devices have emerged as a promising platform for bio‐inspired vision systems, providing unprecedented opportunities to address the limitations of traditional von Neumann architecture by integrating sensing, memory, and computation into a single unit, enabling high efficiency. Despite the fast growth of electronic neuromorphic devices, iontronic neuromorphic devices have demonstrated great potential in processing dynamic visual tasks. By emulating biological ion transport and synaptic signal transmission, they offer inherent biomimicry, low power consumption, and excellent compatibility with soft tissues. This review first analyzes the fundamental working mechanisms of biological vision systems, with a particular focus on ion‐mediated signal transduction across cellular membranes and synapses, which form the theoretical basis for ion‐based neuromorphic devices. We then delve into the working principles of ionic devices in simulating biological vision, theoretically demonstrating the feasibility of constructing neuromorphic visual systems using ionic devices. Next, the review highlights cutting‐edge applications in neuromorphic visual perception and bio‐integrated visual systems based on soft iontronics. By bridging material innovation and biomimetic engineering, soft iontronics provides a roadmap toward energy‐efficient, biocompatible vision systems that harmonize artificial and biological intelligence.

Advanced Materials TechnologiesVol. 11(5)
Shenzhen Polytechnic University (CN), Southern University of Science and Technology (CN)
Shenzhen Polytechnic, National Natural Science Foundation of China, Southern University of Science and Technology, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 21%
Advanced Memory and Neural Computing
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.