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An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.

Xiurui Xie¹, Hong Qu^1,2,3, Guisong Liu¹

¹Department of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, Sichuan, China.

|April 5, 2016

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A new Normalized Spiking Error Back Propagation (NSEBP) algorithm improves training efficiency for hierarchical spiking neural networks (SNNs). This method enhances learning speed and parameter sensitivity in SNNs for cognitive tasks.

Area of Science:

Computational Neuroscience
Artificial Intelligence
Machine Learning

Background:

Spiking neural networks (SNNs), the third generation of neural networks, excel in cognitive tasks like pattern recognition.
Hierarchical structures and temporal encoding in SNNs mimic biological systems, offering strong computational power.
Existing training methods for hierarchical SNNs suffer from low efficiency, gradient diffusion, and parameter sensitivity due to serial processing and back-propagation.

Purpose of the Study:

To introduce a novel training algorithm, Normalized Spiking Error Back Propagation (NSEBP), to enhance the efficiency of hierarchical SNNs.
To maintain the computational capabilities of hierarchical SNNs while overcoming the limitations of current training algorithms.

Main Methods:

NSEBP calculates output spike times by solving quadratic functions in the spike response model, optimizing feedforward computation.

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Error propagation in feedback uses presynaptic spike jitter instead of gradient descent, enabling layer-wise training.

The algorithm establishes a mathematical link between weight variation and voltage error change for applicable weight modification normalization.

Main Results:

NSEBP demonstrates superior learning efficiency compared to traditional multi-layer SNN algorithms.
The proposed algorithm exhibits reduced sensitivity to parameters.
Comprehensive experimental results validate the performance improvements of NSEBP.

Conclusions:

NSEBP effectively addresses the training inefficiencies of hierarchical SNNs.
The algorithm offers a more robust and efficient approach to training SNNs for complex cognitive tasks.
NSEBP represents a significant advancement in SNN training methodologies.