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

Long-term Potentiation01:35

Long-term Potentiation

58.0K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
58.0K
Long-term Potentiation01:25

Long-term Potentiation

3.3K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...
3.3K

You might also read

Related Articles

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

Sort by
Same author

A semi-holographic hyperdimensional representation system for hardware-friendly cognitive computing.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2019
Same author

An electrical characterisation methodology for identifying the switching mechanism in TiO<sub>2</sub> memristive stacks.

Scientific reports·2019
Same author

Extended memory lifetime in spiking neural networks employing memristive synapses with nonlinear conductance dynamics.

Nanotechnology·2018
Same author

Effect of patterned polyacrylamide hydrogel on morphology and orientation of cultured NRVMs.

Scientific reports·2018
Same author

X-ray spectromicroscopy investigation of soft and hard breakdown in RRAM devices.

Nanotechnology·2016
Same author

Spatially resolved TiOx phases in switched RRAM devices using soft X-ray spectromicroscopy.

Scientific reports·2016

Related Experiment Video

Updated: Jan 2, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.3K

STDP and STDP variations with memristors for spiking neuromorphic learning systems.

T Serrano-Gotarredona1, T Masquelier, T Prodromakis

  • 1Department of Analog and Mixed-Signal Design, Instituto de Microelectrónica de Sevilla, IMSE-CNM-CSIC Sevilla, Spain.

Frontiers in Neuroscience
|February 21, 2013
PubMed
Summary

This paper explores asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristors for efficient neural network learning. It details methods for memristive synapses to perform computations and learn without global synchronization, mimicking biological systems.

Keywords:
artificial-learning-synapsesmemristor/cmosspike-timing-dependent-plasticityspiking-neural-networks

More Related Videos

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

8.2K
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

9.2K

Related Experiment Videos

Last Updated: Jan 2, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.3K
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

8.2K
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

9.2K

Area of Science:

  • Neuroscience
  • Materials Science
  • Computer Engineering

Background:

  • Spike-Timing-Dependent-Plasticity (STDP) is crucial for synaptic learning in neural systems.
  • Memristors offer a promising hardware platform for neuromorphic computing due to their analog memory capabilities.
  • Current memristor-based STDP implementations often require global synchronization or separate learning/performing phases.

Purpose of the Study:

  • To review methods for realizing asynchronous STDP using memristors as synapses.
  • To enable memristive synapses to perform synaptic weight multiplications without global synchronization or phase separation.
  • To investigate the implementation of STDP rules in memristor cross-bar architectures for artificial vision.

Main Methods:

  • Reviewing existing literature on memristor-based STDP.
  • Analyzing two memristor physics models: 'moving wall' and 'filament creation/annihilation'.
  • Describing the implementation of pure timing-based and hybrid STDP rules in cross-bar architectures.

Main Results:

  • Demonstrated memristor-based STDP realization without global synchronization or phase separation.
  • Showcased the flexibility of memristors in implementing different STDP rules (timing-based and hybrid).
  • Presented potential applications in artificial vision using memristor cross-bar arrays.

Conclusions:

  • Asynchronous STDP is achievable with memristors, offering a more biologically plausible and efficient approach.
  • Memristor technology can support diverse STDP learning rules and large-scale neural network implementations.
  • These advancements pave the way for more sophisticated neuromorphic computing systems, particularly for vision tasks.