Optically Modulated Nanofluidic Ionic Transistor for Neuromorphic Functions

Neuromorphic systems that can emulate the behavior of neurons have garnered increasing interest across interdisciplinary fields due to their potential applications in neuromorphic computing, artificial intelligence and brain-machine interfaces. However, the optical modulation of nanofluidic ion transport for neuromorphic functions has been scarcely reported. Herein, inspired by biological systems that rely on ions as signal carriers for information perception and processing, we present a nanofluidic transistor based on a metal-organic framework membrane (MOFM) with optically modulated ion transport properties, which can mimic the functions of biological synapses. Through the dynamic modulation of synaptic weight, we successfully replicate intricate learning-experience behaviors and Pavlovian associate learning processes by employing sequential optical stimuli. Additionally, we demonstrate the application of the International Morse Code with the nanofluidic device using patterned optical pulse signals, showing its encoding and decoding capabilities in information processing process. This study would largely advance the development of nanofluidic neuromorphic devices for biomimetic iontronics integrated with sensing, memory and computing functions.

Authors

Institutions

Publication Details

Journal
Angewandte Chemie International Edition
Published
2024-11-26
DOI
https://doi.org/10.1002/anie.202418949
Citations
24
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
5.01
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optically Modulated Nanofluidic Ionic Transistor for Neuromorphic Functions

Lanqun Mao, Xiulan He, Yanan Jiang, Jiahao Lu et al.
24 citations
Angewandte Chemie International Edition
Advanced Memory and Neural Computing
5.01
article

Optically Modulated Nanofluidic Ionic Transistor for Neuromorphic Functions

Lanqun Mao, Xiulan He, Yanan Jiang, Jiahao Lu, Jiao Wang, Tianyi Xiong
article en
24 citations

Abstract

Neuromorphic systems that can emulate the behavior of neurons have garnered increasing interest across interdisciplinary fields due to their potential applications in neuromorphic computing, artificial intelligence and brain-machine interfaces. However, the optical modulation of nanofluidic ion transport for neuromorphic functions has been scarcely reported. Herein, inspired by biological systems that rely on ions as signal carriers for information perception and processing, we present a nanofluidic transistor based on a metal-organic framework membrane (MOFM) with optically modulated ion transport properties, which can mimic the functions of biological synapses. Through the dynamic modulation of synaptic weight, we successfully replicate intricate learning-experience behaviors and Pavlovian associate learning processes by employing sequential optical stimuli. Additionally, we demonstrate the application of the International Morse Code with the nanofluidic device using patterned optical pulse signals, showing its encoding and decoding capabilities in information processing process. This study would largely advance the development of nanofluidic neuromorphic devices for biomimetic iontronics integrated with sensing, memory and computing functions.

Angewandte Chemie International EditionVol. 64(7)
Chinese Academy of Sciences (CN), Beijing Normal University (CN), Beijing National Laboratory for Molecular Sciences (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 4%
Advanced Memory and Neural Computing
5.01
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.