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

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

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...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Propagation of Action Potentials

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...

You might also read

Related Articles

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

Sort by

Same author

Design of a Wobble-Scheme Heterojunction for Catalytic Cancer Therapy.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

Ice-water sheet coverage for rapid core temperature reduction in exertional heat stroke: A case series.

The American journal of emergency medicine·2026

Same author

Atractylenolide I Alleviates Lipotoxicity in Hepatocytes and Protects NAFLD in Mice Through SIRT1-Dependent IRE1α-XBP1 Pathway.

Phytotherapy research : PTR·2026

Same author

Abietic acid targets RAN to restrict coxsackievirus B3 infection.

Frontiers in microbiology·2026

Same author

Discovery and characterization of hinokiflavone as a potent natural CD137 inhibitor for Kawasaki disease.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026

Same author

Exosomal miR-181d-5p derived from distal airway stem cells inhibit apoptosis of pulmonary vascular endothelial cells in COPD mice by targeting PDAP1.

Stem cell research & therapy·2026

Search research articles

Related Experiment Video

Updated: Feb 27, 2026

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

Robustness to Training Disturbances in SpikeProp Learning.

Sumit Bam Shrestha, Qing Song

IEEE Transactions on Neural Networks and Learning Systems

|July 11, 2017

Summary

This study introduces a learning rate normalization scheme to stabilize spiking neural network training with SpikeProp. The method minimizes learning surges and improves training success and speed.

Area of Science:

Artificial Intelligence
Computational Neuroscience

Background:

Spiking neural network (SNN) training using SpikeProp faces stability challenges due to nonlinear neuron dynamics and disturbances.
Learning instability manifests as surges in learning cost, necessitating careful selection of learning steps.

Purpose of the Study:

To develop a method for stabilizing SpikeProp training in spiking neural networks.
To address learning instability caused by nonlinearities and internal/external disturbances.

Main Methods:

Weight convergence analysis of SpikeProp learning under disturbance signals.
Extension to robust stability analysis of overall system error.
Development of a learning rate normalization scheme.

Main Results:

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

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Related Experiment Videos

Last Updated: Feb 27, 2026

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

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

The proposed learning rate normalization scheme ensures bounded total learning error with minimal assumptions on disturbance signals.
Experimental results demonstrate stable learning with minimal surges compared to existing methods.
The scheme leads to higher training success rates and faster learning.

Conclusions:

The developed learning rate normalization scheme effectively enhances the stability and performance of SpikeProp training.
This approach offers a robust solution for training spiking neural networks in the presence of various disturbances.