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Related Experiment Video

Updated: Mar 11, 2026

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

Robust learning in SpikeProp.

Sumit Bam Shrestha¹, Qing Song¹

¹School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.

Neural Networks : the Official Journal of the International Neural Network Society

|November 27, 2016

Summary

This summary is machine-generated.

Spiking neural network training with SpikeProp faces instability due to surges. This study proposes a robust adaptive learning rate to improve convergence and learning speed, enhancing stability in neural network training.

Keywords:

Adaptive learning rate Error analysis Robust stability Spiking Neural Network Supervised learning Weight convergence

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Area of Science:

Artificial Intelligence
Computational Neuroscience

Background:

Spiking Neural Networks (SNNs) offer energy-efficient brain-inspired computation.
Training SNNs using SpikeProp and its variants is prone to instability, characterized by learning cost surges.
External and internal disturbances further destabilize the SNN learning process.

Purpose of the Study:

To analyze the stability of the SpikeProp learning process under disturbances.
To develop a robust adaptive learning rate scheme for SNNs.
To improve the convergence and learning speed of SNN training.

Main Methods:

Performed error system analysis incorporating external disturbances.
Conducted weight convergence and robust stability analysis of the SpikeProp learning process.
Proposed and implemented a robust adaptive learning rate scheme based on theoretical findings.

Main Results:

The proposed robust adaptive learning rate scheme effectively minimizes learning cost surges.
The method demonstrates improved stability and robustness against internal and external disturbances.
Experimental results show superior performance in convergence and learning speed compared to existing methods.

Conclusions:

The robust adaptive learning rate scheme enhances the stability and efficiency of SpikeProp training.
This approach addresses key challenges in SNN training, paving the way for more reliable SNN applications.
The findings contribute to the advancement of stable and efficient SNN training methodologies.