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

Polymer Classification: Architecture01:14

Polymer Classification: Architecture

3.9K
Polymers are classified as linear or branched on the basis of their chain architecture. The polymer chains in linear polymers have a long chain-like structure with minimal to no branching at all. Even if a polymer features large substituent groups on the monomer, which appear as branches to the skeleton, it is not considered a branched polymer. A branched polymer contains secondary polymer chains that arise from the main polymer chain. The branching occurs when the polymer growth shifts from...
3.9K
Synaptic Signaling01:12

Synaptic Signaling

79.8K
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.
79.8K
Synaptic Signaling01:09

Synaptic Signaling

6.7K
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...
6.7K
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Force Classification01:22

Force Classification

2.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.4K

You might also read

Related Articles

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

Sort by
Same author

A hybrid LLM and machine learning framework for early fire detection in subway tunnels.

Scientific reports·2026
Same author

DeepRespNet: a hybrid attention-recurrent framework for non-contact respiratory rate estimation.

Frontiers in physiology·2026
Same author

Association Between Ratio of Cholesterols and Coronary Artery Stenosis According to Low-Density Lipoprotein-Cholesterol Levels and Statin Use.

Korean circulation journal·2026
Same author

Effects of ramped GVS parameter combinations on vestibular perception and their application in a Virtual Reality flight simulator.

Ergonomics·2026
Same author

High-strength and high-modulus silicon monoxide for high-energy-density and fast-charging lithium-ion batteries.

Nature communications·2026
Same author

Enhanced multicancer screening assay through whole-genome methylation sequencing-based multimodal cell-free DNA analysis.

Experimental & molecular medicine·2026
Same journal

Sodium-Based Battery Component Design: Imitating Lithium or Forging New Paths?

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

Enhancing Birefringence of Sulphates by Polarity Modification in Planar Cations.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

In Situ Atomic-Scale Observation of Preferential Premelting at Oxide Crystal Defects.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

Thickness-Dependent Semiconductor-Metal Transition in Two-Dimensional Nonlayered Magnetic CuCo<sub>2</sub>S<sub>4</sub>.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

Programmable Control Over Radical and Non‑Radical Pathways in Fenton‑Like Catalysis via Carbon‑Encapsulated Iron Nanoreactors.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

Self-Powered MXene@Perovskite Thermoelectric Skin for Multimodal Mid-Infrared Sensing and Human Signal Recognition.

Small (Weinheim an der Bergstrasse, Germany)·2026
See all related articles

Related Experiment Video

Updated: Feb 7, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

23.2K

Synaptic Device Network Architecture with Feature Extraction for Unsupervised Image Classification.

Sungho Kim1, Bongsik Choi2, Meehyun Lim3

  • 1Department of Electrical Engineering, Sejong University, Seoul, 05006, South Korea.

Small (Weinheim an Der Bergstrasse, Germany)
|July 17, 2018
PubMed
Summary
This summary is machine-generated.

Neuromorphic systems offer energy-efficient computing for image recognition. This study demonstrates a synaptic network with feature extraction, achieving 90% handwritten digit recognition with fewer devices.

Keywords:
carbon nanotubesfeature extractionimage classificationneuromorphic systemsrecognition rates

More Related Videos

Perfusable Vascular Network with a Tissue Model in a Microfluidic Device
07:05

Perfusable Vascular Network with a Tissue Model in a Microfluidic Device

Published on: April 4, 2018

14.9K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K

Related Experiment Videos

Last Updated: Feb 7, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

23.2K
Perfusable Vascular Network with a Tissue Model in a Microfluidic Device
07:05

Perfusable Vascular Network with a Tissue Model in a Microfluidic Device

Published on: April 4, 2018

14.9K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K

Area of Science:

  • Neuroscience and Computer Science
  • Neuromorphic Engineering
  • Artificial Intelligence

Background:

  • Deep learning excels at image recognition but is energy-intensive on conventional von Neumann architectures.
  • Neuromorphic systems offer a promising, energy-efficient alternative for computing.
  • Challenges remain in understanding synaptic device impacts and implementing feature extraction on neuromorphic hardware.

Purpose of the Study:

  • To demonstrate a neuromorphic system incorporating feature extraction for enhanced pattern recognition.
  • To validate the performance of a synaptic device network architecture for image classification.
  • To address the implementation gap of feature extraction algorithms in neuromorphic systems.

Main Methods:

  • Designed a synaptic device network architecture inspired by convolutional neural networks (CNNs).
  • Integrated a feature extraction algorithm within the neuromorphic network.
  • Validated pattern recognition efficacy through device-to-system level simulations.

Main Results:

  • The proposed network successfully classified handwritten digits.
  • Achieved a recognition rate of up to 90%.
  • Demonstrated superior performance with fewer synaptic devices compared to architectures lacking feature extraction.

Conclusions:

  • Feature extraction integration significantly improves neuromorphic system efficiency and performance for image classification.
  • This approach offers a viable pathway towards energy-efficient, high-performance neuromorphic computing.
  • Further research into synaptic device specifications is crucial for optimizing neuromorphic system design.