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

Neuroplasticity

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.

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.

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...

Plasticity

Plasticity

Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...

You might also read

Related Articles

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

Sort by

Same author

Hedgehog signaling enhances the Schwann-like and nerve repair-supportive properties of ectomesenchymal stem cells.

Frontiers in neuroscience·2026

Same author

Rare Eating Epilepsy: Co-Occurrence of Focal Cortical Dysplasia and Gray Matter Heterotopia.

Neurology·2026

Same author

Multimodal imaging of gene expression, morphology, and activity of the same neuron.

Cell·2026

Same author

Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence.

Science advances·2026

Same author

An online packing strategy for high volume injection in sensitivity-enhanced UPLC-MS/MS analysis of 30 veterinary drugs in beef.

Journal of chromatography. A·2026

Same author

Biologically inspired spiking diffusion model with adaptive lateral selection mechanism.

Neural networks : the official journal of the International Neural Network Society·2026

Search research articles

Related Experiment Video

Updated: Nov 27, 2025

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

GLSNN: A Multi-Layer Spiking Neural Network Based on Global Feedback Alignment and Local STDP Plasticity.

Dongcheng Zhao^1,2, Yi Zeng^1,2,3,4, Tielin Zhang¹

¹Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Frontiers in Computational Neuroscience

|December 7, 2020

Summary

This study introduces a novel method for training Spiking Neural Networks (SNNs) using brain-inspired feedback alignment and STDP. This approach enhances SNN training efficiency and robustness for improved performance on benchmark datasets.

Keywords:

SNN brain global feedback alignment local STDP plasticity

More Related Videos

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

An Approach to Enhance Alignment and Myelination of Dorsal Root Ganglion Neurons

An Approach to Enhance Alignment and Myelination of Dorsal Root Ganglion Neurons

Published on: August 24, 2016

Related Experiment Videos

Last Updated: Nov 27, 2025

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

An Approach to Enhance Alignment and Myelination of Dorsal Root Ganglion Neurons

An Approach to Enhance Alignment and Myelination of Dorsal Root Ganglion Neurons

Published on: August 24, 2016

Area of Science:

Computational Neuroscience
Artificial Intelligence
Machine Learning

Background:

Spiking Neural Networks (SNNs) mimic biological brain processing but face training challenges due to their non-differential nature.
Efficient and robust training of SNNs remains a significant hurdle in advancing neural network technology.

Purpose of the Study:

To develop a novel, biologically-inspired training method for Spiking Neural Networks.
To address the challenges of training non-differential SNNs efficiently and robustly.

Main Methods:

Implemented global random feedback alignment, inspired by top-down brain connections, to propagate error signals.
Utilized differential Spike-Timing-Dependent Plasticity (STDP) to optimize local synaptic plasticity, mimicking biological systems.

Main Results:

Achieved high accuracy on MNIST (98.62%) and Fashion MNIST (89.05%) datasets.
Demonstrated superior performance compared to several state-of-the-art SNNs trained via backpropagation.

Conclusions:

The proposed biologically-plausible method offers an effective alternative for training SNNs.
This approach successfully overcomes key limitations in current SNN training paradigms.