Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

MOS Capacitor01:25

MOS Capacitor

1.1K
A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
1.1K
Resting Membrane Potential01:24

Resting Membrane Potential

19.9K
The relative difference in electrical charge, or voltage, between the inside and the outside of a cell membrane, is called the membrane potential. It is generated by differences in permeability of the membrane to various ions and the concentrations of these ions across the membrane.
The Inside of a Neuron is More Negative
The membrane potential of a cell can be measured by inserting a microelectrode into a cell and comparing the charge to a reference electrode in the extracellular fluid. The...
19.9K

You might also read

Related Articles

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

Sort by
Same author

The Prognostic Value of <sup>68</sup>Ga-PSMA and <sup>18</sup>F-FDG PET/CT in Metastatic Castration-Resistant Prostate Cancer Treated with <sup>177</sup>Lu-EB-PSMA.

Annals of nuclear medicine·2026
Same author

Modulating proton-coupled electron transfer in 2D metal-covalent organic frameworks for garnering meliorated dual-function photocatalysis.

Journal of colloid and interface science·2026
Same author

Beyond Crystallinity: How Amorphous Structures Dictate Performance in Electrocatalysis.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Chloride-induced easier phase transformation and catalytic synergy for enhanced seawater splitting.

Chemical science·2026
Same author

Composition and Structural Design of Magnetic Alloy/Composites for High-Performance Microwave Absorption: A Review.

Nanomaterials (Basel, Switzerland)·2026
Same author

Regulating Cu Atom Dispersity on Nitrogen-Doped Carbon for Boosting Electrocatalytic Nitrate Reduction in Strongly Acidic Media.

Small (Weinheim an der Bergstrasse, Germany)·2026

Related Experiment Video

Updated: Oct 9, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

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

Published on: March 9, 2019

8.0K

Emerging dynamic memristors for neuromorphic reservoir computing.

Jie Cao1,2, Xumeng Zhang1,3,2, Hongfei Cheng4

  • 1Frontier Institute of Chip and System, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China. wang_ming@fudan.edu.cn.

Nanoscale
|December 21, 2021
PubMed
Summary

Dynamic memristors show promise for hardware implementation of reservoir computing (RC) systems, enabling faster and more energy-efficient temporal data analysis. This review highlights their potential and discusses challenges for future advancements in neuromorphic computing.

More Related Videos

A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.1K
In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx
09:49

In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx

Published on: May 13, 2020

4.2K

Related Experiment Videos

Last Updated: Oct 9, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

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

Published on: March 9, 2019

8.0K
A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.1K
In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx
09:49

In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx

Published on: May 13, 2020

4.2K

Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Computer Science

Background:

  • Reservoir computing (RC) offers efficient temporal data analysis but faces hardware limitations.
  • Dynamic memristors exhibit properties suitable for neuromorphic hardware, including speed, low energy, and memory.

Purpose of the Study:

  • To review advancements in using dynamic memristors for hardware-accelerated reservoir computing.
  • To identify key memristor parameters influencing RC system performance.

Main Methods:

  • Review of research leveraging dynamic memristors in RC systems.
  • Analysis of resistive switching and magnetoresistive memristor devices for RC applications.
  • Discussion of memristor characteristics like reservoir size and decay time.

Main Results:

  • Dynamic memristors enhance data processing in RC systems based on resistive and magnetoresistive devices.
  • Key parameters affecting RC performance, such as reservoir size and decay time, are identified.
  • Striking results demonstrating the efficacy of memristor-based RC are presented.

Conclusions:

  • Dynamic memristors are a promising technology for efficient hardware implementation of reservoir computing.
  • Challenges remain in achieving reliable and accurate task processing with memristor-based RC systems.
  • Future research directions focus on overcoming these challenges and advancing RC capabilities.