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

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

You might also read

Related Articles

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

Sort by
Same author

Vanadium Nitride Quantum-Dot Bidirectional Catalysis for Accelerated Polysulfide Redox in Room-Temperature Na-S Batteries.

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

Hepatic Congestion-linked Intrahepatic Biliary Strictures After Living Donor Liver Transplantation.

Transplantation·2026
Same author

Factors associated with hip deformity in children with nonambulatory spastic cerebral palsy.

Medicine·2026
Same author

Molecular-level analysis of chemical transformation of algal extracellular organic matter during seawater ozonation: Dominant reaction pathways and impacts of halide ions.

Water research·2026
Same author

Anatomical risk stratification for major portal vein complications in dual portal vein living donor liver transplantation: a retrospective cohort study.

Annals of surgical treatment and research·2026
Same author

A machine learning perspective on three decades of methanol synthesis: research framework and experimental operation insights.

Communications engineering·2026

Related Experiment Video

Updated: Jun 27, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network.

Hyunho Seok1,2, Shihoon Son1,2, Sagar Bhaurao Jathar1,2

  • 1SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea.

Sensors (Basel, Switzerland)
|March 30, 2023
PubMed
Summary

Memristors, mimicking brain synapses, enable efficient neuromorphic computing. Layered 2D materials show promise for low-power, brain-inspired artificial intelligence hardware, overcoming traditional computing limits.

Keywords:
artificial intelligencebioinspired deviceconvolutional neural networkmemristorneuromorphic computingresistive random-access memorysynapse

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

9.0K
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

7.9K

Related Experiment Videos

Last Updated: Jun 27, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K
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

9.0K
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

7.9K

Area of Science:

  • Materials Science
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Traditional von Neumann architecture faces limitations in power consumption and integration density due to continuous data transport.
  • Biological synapses provide a model for efficient information transfer, inspiring neuromorphic computing.
  • Memristors, functioning as resistive random-access memory (RRAM), are key components for emulating synaptic functions in hardware.

Purpose of the Study:

  • To review the memristive characteristics of various two-dimensional (2D) materials for neuromorphic computing applications.
  • To highlight the potential of 2D materials in overcoming the limitations of von Neumann architecture for artificial intelligence.
  • To discuss the use of 2D material-based memristors in image segregation and pattern recognition.

Main Methods:

  • Review of literature on memristive properties of 2D materials including heterostructures, defect-engineered, and alloy materials.
  • Analysis of the application of these materials in neuromorphic computing hardware.
  • Discussion of hardware-implemented Convolutional Neural Networks (CNNs) utilizing synaptic memristor arrays.

Main Results:

  • Layered 2D materials exhibit significant potential for low-power computing due to their electronic and physical properties.
  • Memristor arrays based on 2D materials offer biomimetic in-memory processing capabilities.
  • These advancements are crucial for meeting the increasing computational demands of artificial intelligence.

Conclusions:

  • Neuromorphic computing using 2D material memristors represents a breakthrough for artificial intelligence, offering enhanced performance and lower power consumption.
  • Hardware solutions based on non-von Neumann architectures, particularly CNNs with synaptic memristor arrays, are promising for future electronics.
  • This emerging paradigm facilitates edge computing and deep neural networks through hardware-centric approaches.