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

2.0K
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...
2.0K
Long-term Potentiation01:35

Long-term Potentiation

56.6K
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.
56.6K

You might also read

Related Articles

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

Sort by
Same author

Energy landscapes and synergetic state transitions in frustrated Stuart-Landau oscillator networks: a homotopy continuation study.

Frontiers in network physiology·2026
Same author

CauFinder: Steering Cell-State and Phenotype Transitions by Causal Disentanglement Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Identifying the optimal rapid antigen test for screening and determining the end of isolation: A modeling study.

PLoS computational biology·2026
Same author

Force Learning in Balanced Cortical E-I Networks.

Neural computation·2026
Same author

Psychological distress among Japanese high school students during the COVID-19 pandemic: An energy landscape analysis.

PLoS medicine·2026
Same author

Stratification of viral shedding patterns in saliva of COVID-19 patients.

eLife·2026

Related Experiment Video

Updated: Oct 27, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.2K

A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward

Yusuke Sakemi, Kai Morino, Takashi Morie

    IEEE Transactions on Neural Networks and Learning Systems
    |July 19, 2021
    PubMed
    Summary
    This summary is machine-generated.

    We introduce a new supervised learning algorithm for Spiking Neural Networks (SNNs) that uses temporal coding for energy-efficient analog VLSI implementation. This method achieves high accuracy on image recognition tasks, comparable to existing SNN algorithms.

    More Related Videos

    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
    07:34

    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

    Published on: March 25, 2014

    10.0K
    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

    10.5K

    Related Experiment Videos

    Last Updated: Oct 27, 2025

    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
    05:19

    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

    Published on: November 12, 2019

    7.2K
    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
    07:34

    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

    Published on: March 25, 2014

    10.0K
    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

    10.5K

    Area of Science:

    • Artificial Intelligence
    • Neuroscience
    • Computer Engineering

    Background:

    • Spiking Neural Networks (SNNs) are brain-inspired models processing information via spikes.
    • SNNs offer potential for novel machine learning and energy-efficient computation in Very-Large-Scale Integration (VLSI) circuits.

    Purpose of the Study:

    • To propose a novel supervised learning algorithm for SNNs utilizing temporal coding.
    • To design spiking neurons for ultra-high energy efficiency in analog VLSI with resistive memory.
    • To enhance recognition task performance and assess robustness against manufacturing variations.

    Main Methods:

    • Developed a supervised learning algorithm for SNNs based on temporal coding.
    • Designed spiking neurons optimized for analog VLSI and resistive memory.
    • Implemented techniques to improve recognition performance and robustness.

    Main Results:

    • Achieved classification accuracy comparable to state-of-the-art SNN algorithms on MNIST and Fashion-MNIST datasets.
    • Demonstrated the potential for ultra-high energy efficiency through analog VLSI implementation.
    • Evaluated and discussed the robustness of SNNs against device manufacturing variations.

    Conclusions:

    • The proposed temporal coding SNN algorithm offers high performance and energy efficiency for analog VLSI.
    • The algorithm shows promise for robust image recognition despite manufacturing variations.
    • This work contributes to the development of efficient and practical SNNs.