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Related Experiment Video

Updated: May 10, 2026

A Method for Growing Bio-memristors from Slime Mold

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Published on: November 2, 2017

Emerging Liquid-Based Memristive Devices for Neuromorphic Computation.

Qinyang Fan^1,2, Jianyu Shang^1,2, Xiaoxuan Yuan^1,2

¹Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China.

|March 18, 2025

Summary

Liquid-based memristors offer a biocompatible, low-energy alternative to solid-state devices for neuromorphic computing. These novel components mimic biological systems, paving the way for advanced artificial intelligence hardware.

Keywords:

bionics ion transport iontronics liquid‐based memristors neuromorphic devices

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Area of Science:

Materials Science
Neuroscience
Computer Engineering

Background:

Neuromorphic computing aims to replicate human brain functions using hardware.
Memristors are key components for emulating artificial neurons and synapses.
Current solid-state memristors differ from the aqueous environment of biological nervous systems.

Purpose of the Study:

To review recent advancements in liquid-based memristors for neuromorphic computing.
To discuss the operating mechanisms, structures, and functionalities of these devices.
To propose future applications and development directions.

Main Methods:

Review of experimental demonstrations of liquid-based memristors.
Analysis of operating principles and device characteristics.
Exploration of potential applications in artificial intelligence.

Main Results:

Liquid-based memristors exhibit unique memristive properties and neuromorphic functionalities.
These devices offer advantages over solid-state memristors, including anti-interference, low energy consumption, and low heat generation.
Excellent biocompatibility makes them suitable for next-generation AI systems.

Conclusions:

Liquid-based memristors represent a promising direction for developing bio-analogous and energy-efficient neuromorphic computing systems.
Further research into their applications and development is crucial for advancing artificial intelligence.
These devices bridge the gap between biological and artificial systems for intelligent computation.