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

Electrical Synapses01:28

Electrical Synapses

8.3K
Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
8.3K
Propagation of Action Potentials01:23

Propagation of Action Potentials

5.7K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
5.7K
Magnetic Field Due To A Thin Straight Wire01:28

Magnetic Field Due To A Thin Straight Wire

4.8K
Consider an infinitely long straight wire carrying a current I. The magnetic field at point P at a distance a from the origin can be calculated using the Biot-Savart law.
4.8K

You might also read

Related Articles

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

Sort by
Same author

Coherent quantum control of nitrogen vacancy spin with nanoscale magnets.

Nature communications·2026
Same author

Strain Mediated Voltage Control of Magnetic Anisotropy and Magnetization Reversal in Bismuth-Substituted Yttrium Iron Garnet Films and Mesostructures.

ACS applied materials & interfaces·2025
Same author

Magneto-Ionic Physical Reservoir Computing in Perpendicularly Magnetized Heterostructures.

Nano letters·2025
Same author

Quantized artificial neural networks implemented with spintronic stochastic computing.

Nanotechnology·2025
Same author

Self-assembled 3D Interconnected Magnetic Nanowire Networks for Neuromorphic Computing.

ACS applied materials & interfaces·2025
Same author

Antiferromagnetic skyrmion-based energy-efficient leaky integrate and fire neuron device.

Nanotechnology·2025

Related Experiment Video

Updated: Jul 1, 2025

Fabrication of Magnetic Platforms for Micron-Scale Organization of Interconnected Neurons
09:54

Fabrication of Magnetic Platforms for Micron-Scale Organization of Interconnected Neurons

Published on: July 14, 2021

4.8K

Quantized Magnetic Domain Wall Synapse for Efficient Deep Neural Networks.

Seema Dhull, Walid Al Misba, Arshid Nisar

    IEEE Transactions on Neural Networks and Learning Systems
    |March 12, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study demonstrates efficient on-chip neural network training and inference using magnetic domain wall (DW) synaptic arrays. The quantized neural network (QNN) achieves high accuracy while significantly improving area, energy, and latency compared to CMOS designs.

    More Related Videos

    Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
    07:42

    Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

    Published on: July 20, 2022

    2.7K
    Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
    05:39

    Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

    Published on: August 2, 2019

    9.6K

    Related Experiment Videos

    Last Updated: Jul 1, 2025

    Fabrication of Magnetic Platforms for Micron-Scale Organization of Interconnected Neurons
    09:54

    Fabrication of Magnetic Platforms for Micron-Scale Organization of Interconnected Neurons

    Published on: July 14, 2021

    4.8K
    Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
    07:42

    Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

    Published on: July 20, 2022

    2.7K
    Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
    05:39

    Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

    Published on: August 2, 2019

    9.6K

    Area of Science:

    • Spintronics
    • Neuromorphic Computing
    • Solid-State Circuits

    Background:

    • Quantized synaptic weights using nonvolatile memory (NVM) offer efficient neural networks for hardware.
    • Challenges include limited quantized states, device variation, and stochasticity in NVM devices.

    Purpose of the Study:

    • To present on-chip training and inference of a neural network using a magnetic domain wall (DW)-based synaptic array.
    • To evaluate the performance of this quantized neural network (QNN) architecture considering device nonidealities.

    Main Methods:

    • Utilized a rigorous model of magnetic DW devices, including stochasticity and process variations.
    • Implemented DW pinning via physical constrictions for stable quantized weights.
    • Simulated VGG8 architecture for CIFAR-10 image classification using extracted synaptic device characteristics.

    Main Results:

    • Achieved 92.4% training accuracy and 90.4% inference accuracy for the QNN.
    • Demonstrated significant improvements in area, energy, and latency compared to pure CMOS designs.
    • Evaluated performance metrics considering process variations and nonidealities in DW devices and peripheral circuits.

    Conclusions:

    • The proposed QNN architecture enables efficient on-chip learning with magnetic DW synapses.
    • This approach offers substantial advantages in area, energy, and latency for resource-constrained hardware.
    • The study highlights the potential of DW-based NVM for practical neuromorphic computing applications.