Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Long-term Potentiation

Long-term Potentiation

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.

You might also read

Related Articles

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

Sort by

Same author

Competition and cooperation of assembly sequences in recurrent neural networks.

PLoS computational biology·2025

Same author

Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.

Frontiers in neuroinformatics·2024

Same author

On computational models of theory of mind and the imitative reinforcement learning in spiking neural networks.

Scientific reports·2024

Same author

StrongestPath: a Cytoscape application for protein-protein interaction analysis.

BMC bioinformatics·2021

Same author

SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron.

Frontiers in neuroscience·2019

Same author

First-Spike-Based Visual Categorization Using Reward-Modulated STDP.

IEEE transactions on neural networks and learning systems·2018

Search research articles

Related Experiment Video

Updated: Jun 25, 2025

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

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

Published on: November 12, 2019

Bioplausible Unsupervised Delay Learning for Extracting Spatiotemporal Features in Spiking Neural Networks.

Alireza Nadafian¹, Mohammad Ganjtabesh²

¹School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran alirezanadafian@ut.ac.ir.

Neural Computation

|May 22, 2024

Summary

This study introduces a novel unsupervised learning rule for adjusting synaptic delays in spiking neural networks, crucial for temporal feature learning. The rule efficiently extracts spatiotemporal patterns, advancing brain-inspired computing.

More Related Videos

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

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

Published on: March 25, 2014

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

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

Published on: March 2, 2015

Related Experiment Videos

Last Updated: Jun 25, 2025

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

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

Published on: November 12, 2019

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

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

Published on: March 25, 2014

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

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

Published on: March 2, 2015

Area of Science:

Computational Neuroscience
Artificial Intelligence

Background:

Synaptic plasticity, specifically the modulation of conduction delays between neurons, is vital for learning temporal features essential for processing complex data like speech and video.
Understanding the brain's mechanisms for modulating these delays is crucial for developing advanced computational models.

Purpose of the Study:

To propose an unsupervised, bioplausible learning rule for adjusting synaptic delays in spiking neural networks.
To provide mathematical proofs for the convergence of the proposed rule in learning spatiotemporal patterns.
To demonstrate the rule's effectiveness in extracting spatiotemporal features.

Main Methods:

Development of a novel unsupervised learning rule for synaptic delay adjustment in spiking neural networks.
Mathematical derivation and proof of the rule's convergence for spatiotemporal pattern learning.
Experimental validation using random dot kinematogram and DVS128 Gesture datasets.

Main Results:

The proposed learning rule effectively adjusts synaptic delays in spiking neural networks.
Mathematical proofs confirm the rule's convergence for learning spatiotemporal patterns.
Experiments demonstrated the rule's efficiency in extracting spatiotemporal features from real-world data.

Conclusions:

The developed unsupervised learning rule offers an efficient method for synaptic delay plasticity in spiking neural networks.
This approach contributes to the development of more powerful brain-inspired computational models for temporal information processing.
The rule shows promise for applications requiring the extraction of complex spatiotemporal features.