Improved SpikeProp for using particle swarm optimization

• Main Authors: Ahmed, Falah Y. H., Shamsuddin, Siti Mariyam, Mohd. Hashim, Siti Zaiton
• Format: Article
• Language: English
• Published: Hindawi Publishing Corporation 2013
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/48995/1/SitiMariyamShamsuddin2013_ImprovedSpikePropforusingparticle.pdf; http://eprints.utm.my/id/eprint/48995/; http://dx.doi.org/10.1155/2013/257085
• Institution: Universiti Teknologi Malaysia

A spiking neurons network encodes information in the timing of individual spike times. A novel supervised learning rule for SpikeProp is derived to overcome the discontinuities introduced by the spiking thresholding. This algorithm is based on an error-backpropagation learning rule suited for supervised learning of spiking neurons that use exact spike time coding. The SpikeProp is able to demonstrate the spiking neurons that can perform complex nonlinear classification in fast temporal coding. This study proposes enhancements of SpikeProp learning algorithm for supervised training of spiking networks which can deal with complex patterns. The proposed methods include the SpikeProp particle swarm optimization (PSO) and angle driven dependency learning rate. These methods are presented to SpikeProp network for multilayer learning enhancement and weights optimization. Input and output patterns are encoded as spike trains of precisely timed spikes, and the network learns to transform the input trains into target output trains. With these enhancements, our proposed methods outperformed other conventional neural network architectures