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

55.4K
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.
55.4K
Resting Potential Decay01:15

Resting Potential Decay

4.9K
The resting membrane potential of a neuron (-70mV) is sustained due to the selective ion permeability of the membrane. At the resting potential, the membrane is slightly permeable to ions like sodium (Na+) and chloride (Cl−) and highly permeable to potassium ions (K+). Differences in the ions' concentration inside the cell compared to the outside are maintained by membrane transport proteins like channels and pumps.
At rest, the K+ is the main ion that moves across the membrane...
4.9K
MOS Capacitor01:25

MOS Capacitor

860
A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
860
Long-term Depression01:03

Long-term Depression

2.6K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over...
2.6K
The Resting Membrane Potential01:21

The Resting Membrane Potential

133.2K
Overview
133.2K
Multimachine Stability01:25

Multimachine Stability

198
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
198

You might also read

Related Articles

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

Sort by
Same author

Moderate hypofractionation: long-term toxicity results of prostate bed radiotherapy (FRAME-PROSTATE).

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico·2026
Same author

Present and Future of Mosquito-Borne Disease Control in Europe with a Specific Focus on the Mediterranean.

Insects·2026
Same author

Spatial Dynamics and Sterilization Range of Incompatible <i>Aedes albopictus</i> Males: Advancing Toward an Optimized IIT Approach.

Tropical medicine and infectious disease·2026
Same author

Assessing the Reliability of Automatic Milking Systems Data to Support Genetic Improvement in Dairy Cattle.

Animals : an open access journal from MDPI·2026
Same author

Problem difficulty and expertise modulate planning performance in a virtually embodied task.

Journal of neurophysiology·2025
Same author

Permissiveness of different TMEM154 genotype cell lines to different SRLV genotypes/subtypes.

Journal of virology·2025

Related Experiment Video

Updated: Jul 27, 2025

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

Complete Stability of Neural Networks With Extended Memristors.

Mauro Di Marco, Mauro Forti, Riccardo Moretti

    IEEE Transactions on Neural Networks and Learning Systems
    |June 6, 2023
    PubMed
    Summary

    This study analyzes delayed neural networks with Stanford memristors, proving complete stability using Lyapunov methods. The findings ensure vanishing capacitor voltages and power, enabling efficient in-memory computing with nonvolatile memristors.

    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
    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

    Related Experiment Videos

    Last Updated: Jul 27, 2025

    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
    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
    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

    Area of Science:

    • Computational Neuroscience
    • Nanotechnology
    • Materials Science

    Background:

    • Delayed neural networks (NNs) are crucial for complex computations.
    • Memristors, particularly those modeled by the Stanford model, offer nonvolatile memory capabilities.
    • Integrating memristors into NNs presents challenges due to their unique dynamics and potential for multiple equilibrium points.

    Purpose of the Study:

    • To investigate the complete stability (CS) of delayed neural networks incorporating memristors.
    • To develop robust conditions for stability analysis applicable to memristor-based NNs.
    • To explore the implications for power consumption and in-memory computing.

    Main Methods:

    • Lyapunov method for stability analysis.
    • Modeling memristor dynamics using the Stanford model.
    • Analysis of differential variational inequalities for systems with constrained state variables.
    • Verification through numerical simulations.

    Main Results:

    • Derived conditions for complete stability in delayed NNs with Stanford memristors.
    • Demonstrated robustness of stability conditions to interconnection variations and delay values.
    • Showcased vanishing capacitor voltages and NN power, leading to reduced energy consumption.
    • Confirmed the potential for in-memory computing due to nonvolatile memristor properties.

    Conclusions:

    • The study establishes theoretical foundations for stable memristor-based neural networks.
    • The derived conditions offer practical methods (LMI or analytical) for verifying network stability.
    • The research highlights the dual benefit of energy efficiency and computational retention in memristor NNs.