Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

The Resting Membrane Potential

The Resting Membrane Potential

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Diffusive memristors in the edge of chaos.

Nature communications·2026

Same author

Printed devices turn neuromorphic.

Nature nanotechnology·2026

Same author

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing.

Journal of the American Chemical Society·2026

Same author

High-temperature memristors enabled by interfacial engineering.

Science (New York, N.Y.)·2026

Same author

Programmable ferroelectric rectifier for reliable and efficient neuromorphic crossbar array.

Nature communications·2026

Same author

Electrothermally Induced Channel Formation in a Spin-Crossover Neuron.

ACS nano·2026

Same journal

Synthetic Porous Carbons for High-Energy, High-Power Supercapacitors.

Chemical reviews·2026

Same journal

Navigating Misfolded Terrain: ER-Associated Degradation of Membrane Proteins.

Chemical reviews·2026

Same journal

Ink Design for Printing Perovskite Solar Cells and Modules.

Chemical reviews·2026

Same journal

Advanced Single-Atom Catalysts for Thermal-Catalytic C1 Chemistry.

Chemical reviews·2026

Same journal

Copper-Dependent Polysaccharide Monooxygenases: Mechanism and Function.

Chemical reviews·2026

Same journal

To Biotic or Abiotic: Biohybrid Systems for Artificial Photosynthesis.

Chemical reviews·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 4, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

Memristive Ion Dynamics to Enable Biorealistic Computing.

Ruoyu Zhao¹, Seung Ju Kim¹, Yichun Xu¹

¹Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States.

Chemical Reviews

|December 27, 2024

Summary

This summary is machine-generated.

Ion-based memristive devices (IMDs) offer a promising alternative to traditional transistors for artificial intelligence (AI) computing. Their bio-inspired design enables efficient, neuromorphic learning and computation, overcoming current AI bottlenecks.

More Related Videos

A Method for Growing Bio-memristors from Slime Mold

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Related Experiment Videos

Last Updated: Jun 4, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

A Method for Growing Bio-memristors from Slime Mold

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Area of Science:

Materials Science
Computer Engineering
Neuroscience

Background:

Conventional artificial intelligence (AI) faces computational bottlenecks due to mismatches with traditional transistor-based hardware.
Biological neural networks offer efficient, adaptable computation models.
Novel computing paradigms are needed to overcome limitations of current AI architectures.

Purpose of the Study:

To review the fundamental mechanisms and diverse dynamics of ion-based memristive devices (IMDs).
To explore the potential of IMDs for neuromorphic computing and bio-inspired algorithms.
To identify challenges and future research directions for IMDs.

Main Methods:

Review of fundamental mechanisms of IMDs based on ion drift and diffusion.
Examination of material properties influencing IMD switching behaviors.
Analysis of IMD tuning for customized dynamics and applications.

Main Results:

IMDs exhibit diverse dynamics originating from ion drift and diffusion mechanisms.
Different materials enable IMDs with various switching behaviors for diverse applications.
IMDs can be tuned for customized dynamics, suiting bio-inspired algorithms.

Conclusions:

IMDs show significant promise for implementing neuromorphic learning and computing algorithms.
These devices offer a viable hardware solution for overcoming AI computational bottlenecks.
Addressing current challenges in IMD technology is crucial for widespread adoption.