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

Synaptic Signaling01:12

Synaptic Signaling

79.3K
Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
79.3K
Synaptic Signaling01:09

Synaptic Signaling

6.6K
Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
6.6K
Phase Transitions02:31

Phase Transitions

23.1K
Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
23.1K
Phase Diagrams02:39

Phase Diagrams

50.0K
A phase diagram combines plots of pressure versus temperature for the liquid-gas, solid-liquid, and solid-gas phase-transition equilibria of a substance. These diagrams indicate the physical states that exist under specific conditions of pressure and temperature and also provide the pressure dependence of the phase-transition temperatures (melting points, sublimation points, boiling points). Regions or areas labeled solid, liquid, and gas represent single phases, while lines or curves represent...
50.0K
Global Climate Change01:50

Global Climate Change

28.9K
Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
28.9K
Work Done During Volume Change01:17

Work Done During Volume Change

5.1K
In mechanics, work is done on an object when the force acting on it displaces the object. In thermodynamics, work done on a system can be estimated when the system's volume changes during any thermodynamic process.
Consider a gas confined to a cylinder fitted with a movable piston at one end. If the gas expands from volume V1 to volume V2, it exerts a force on the piston, such that the piston moves by a distance dr.
The work done by the gas on the piston can be expressed as
5.1K

You might also read

Related Articles

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

Sort by
Same author

The effects and mechanisms of modified Xiaoyaosan on chronic unpredictable mild stress (CUMS)-induced depressive mice based on network pharmacology.

Journal of ethnopharmacology·2024
Same author

The relationship between Listeria infections and host immune responses: Listeriolysin O as a potential target.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2024
Same author

Dynamic coordination engineering of 2D PhenPtCl<sub>2</sub> nanosheets for superior hydrogen evolution.

Nature communications·2024
Same author

Mesoporous Polyimide Thin Films as Dendrite-Suppressing Separators for Lithium-Metal Batteries.

ACS nano·2023
Same author

Acupuncture for vitiligo: An overview of systematic reviews and meta-analysis.

Journal of cosmetic dermatology·2023
Same author

AMPK-HIF-1α signaling enhances glucose-derived de novo serine biosynthesis to promote glioblastoma growth.

Journal of experimental & clinical cancer research : CR·2023
Same journal

Lasing characteristics and stress-tuning effects in GaN beam microcavities.

Nanoscale·2026
Same journal

Unraveling the synergy of core doping and the motif shell in atomically precise PtAg nanoclusters for CF<sub>3</sub>-ketone alkynylation.

Nanoscale·2026
Same journal

A dual-functional heavy-metal-free quantum dot/TiO<sub>2</sub> hybrid system for simultaneous pollutant degradation and green hydrogen production.

Nanoscale·2026
Same journal

Rational design of spherical NiCoB@rGO nanocomposites for efficient electrochemical energy storage.

Nanoscale·2026
Same journal

Ligand-controlled engineering of Cu-H active sites on Cu<sub>25</sub> hydride nanoclusters for efficient CO<sub>2</sub> electroreduction.

Nanoscale·2026
Same journal

Isostructural Co/Ni-containing banana-shaped polyoxometalates for visible-light-driven hydrogen production.

Nanoscale·2026
See all related articles

Related Experiment Video

Updated: Jan 28, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.7K

Phase-change nanoclusters embedded in a memristor for simulating synaptic learning.

Qin Wan1, Fei Zeng, Jun Yin

  • 1Key Laboratory of Advanced Materials (MOE), School of Materials Science and Engineering, China.

Nanoscale
|March 12, 2019
PubMed
Summary
This summary is machine-generated.

This study presents a novel memristor mimicking biological synapses. Its unique structure and dual-kinetics mechanism enable brain-like computing and support diverse learning protocols for artificial intelligence.

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

9.3K
High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning
10:36

High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning

Published on: December 15, 2016

11.0K

Related Experiment Videos

Last Updated: Jan 28, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.7K
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

9.3K
High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning
10:36

High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning

Published on: December 15, 2016

11.0K

Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Biological synapses exhibit complex dynamics crucial for learning and memory.
  • Existing memristive devices often lack the nuanced kinetic behaviors of biological systems.
  • Developing artificial synapses is key for advancing brain-inspired computing.

Purpose of the Study:

  • To design and fabricate a novel memristor device that emulates the functional dynamics of biological synapses.
  • To investigate the resistive switching mechanism involving oxygen vacancy migration and phase-change nanoclusters.
  • To evaluate the memristor's potential for implementing learning rules in neuromorphic computing.

Main Methods:

  • Fabrication of a Palladium/Niobium-doped Aluminum Oxynitride/Palladium (Pd/Nb:AlNO/Pd) memristor.
  • Analysis of cross-sectional profiles to confirm embedded phase-change Niobium Oxide (NbO) nanoclusters.
  • Characterization of resistive switching mechanisms, including oxygen vacancy (VO) migration and NbO nanocluster structural evolution.
  • Application of pulse stimulations to assess kinetic responses and long-term plasticity.

Main Results:

  • The memristor's filaments are confirmed to be embedded by phase-change NbO nanoclusters.
  • The resistive switching mechanism involves both VO migration and the structural evolution of NbO nanoclusters.
  • The device exhibits dual kinetics, mimicking pre- and post-synaptic dynamics.
  • The memristor successfully responded to complex pulse patterns and demonstrated suitability for spike-rate and spike-timing-dependent plasticity simulations.

Conclusions:

  • The developed memristor serves as an elementary cell that closely approximates biological synapses.
  • The device's ability to replicate synaptic dynamics and learning protocols makes it suitable for brain-like computing applications.
  • This research contributes to the development of more efficient and biologically plausible artificial neural networks.