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A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks.

Yan Xu¹, Xiaoqin Zeng, Lixin Han

¹Institute of Intelligence Science and Technology, Hohai University, Nanjing 210098, PR China. xuyanhehai@163.com

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

|March 19, 2013

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This study introduces a novel multi-spike learning algorithm for spiking neural networks (SNNs), improving accuracy in tasks requiring multiple output spikes. The new method enhances biological neuron simulation and classification performance.

Area of Science:

Computational Neuroscience
Artificial Intelligence

Background:

Spiking Neural Networks (SNNs) mimic biological neurons using temporal encoding.
Existing gradient-descent methods struggle with multi-spike learning in SNNs due to error function and spike interference issues.

Purpose of the Study:

To develop a supervised multi-spike learning algorithm for SNNs that overcomes limitations of current methods.
To enhance the simulation of biological neuron learning mechanisms and improve SNN performance in classification tasks.

Main Methods:

A novel multi-spike learning algorithm for SNNs based on gradient descent was proposed.
Addressed challenges in error function construction and interference among multiple output spikes.
Developed an output encoding strategy for multi-spike classification.

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Main Results:

The proposed method achieves high learning accuracy, especially with a large number of output spikes.
Overcame learning interference issues inherent in multi-spike learning.
Significantly improved classification accuracy compared to single-spike learning methods.

Conclusions:

The new multi-spike learning algorithm offers a robust solution for SNNs requiring precise temporal coding.
The method demonstrates broad applicability for single neuron learning and multilayer SNN classification.
Enhanced SNNs' ability to learn complex temporal patterns and improve classification performance.