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.
Hebbian LTP
LTP can occur when...

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

The effect of anthropometrics and maturity status on youth rugby head impact biomechanics.

Journal of biomechanics·2026

Same author

Reflecting on Themes From Telehealth Consultations Early in the Pandemic: An Opportunity to Learn From Multiprofessional Colleagues.

International journal of telemedicine and applications·2026

Same author

A Scalable Perovskite Platform With Multi-State Photoresponsivity for In-Sensor Saliency Detection.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Effects of One Season of Rugby on the Neurological Integrity of Male Adolescent Players.

Sports medicine - open·2026

Same author

Self-Rectifying Memristors Based on Dimensionally Graded Halide Perovskites.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

A 1024-Channel 0.8V 23.9-nW/Channel Event-Based Compute In-Memory Neural Spike Detector.

IEEE transactions on biomedical circuits and systems·2025

Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026

Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026

Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026

Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026

Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026

Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 16, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

Hardware-amenable structural learning for spike-based pattern classification using a simple model of active

Shaista Hussain¹, Shih-Chii Liu, Arindam Basu

¹School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798.

Neural Computation

|March 4, 2015

Summary

This summary is machine-generated.

This study introduces a novel spike-based neural model for binary pattern classification. This neuromorphic system achieves comparable performance to existing methods while significantly reducing computational resource requirements.

More Related Videos

Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites

Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites

Published on: September 27, 2024

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

Related Experiment Videos

Last Updated: Apr 16, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites

Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites

Published on: September 27, 2024

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

Area of Science:

Computational Neuroscience
Machine Learning

Background:

Classifying high-dimensional binary patterns is computationally intensive.
Existing methods like Support Vector Machines and Extreme Learning Machines require substantial resources.

Purpose of the Study:

To present a novel spike-based neural model for efficient binary pattern classification.
To develop a hardware-friendly learning algorithm inspired by biological structural plasticity.

Main Methods:

A spike-based model with functionally distinct dendritic compartments was developed.
A margin-enhancing, hardware-friendly learning algorithm inspired by structural plasticity was employed.
The model utilizes sparse synaptic connectivity and nonlinear input processing within dendritic subunits.

Main Results:

The proposed model achieved performance comparable to Support Vector Machines and Extreme Learning Machines.
The model demonstrated a reduction of 10% to 50% in computational resource usage.
A branch-specific spike-based structural plasticity rule was also presented.

Conclusions:

The novel spike-based model offers an efficient alternative for binary pattern classification.
The biologically inspired learning algorithm facilitates integration into neuromorphic systems with low overhead.
This approach significantly reduces computational resource demands in machine learning tasks.