DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons

• Published in: IEEE transaction on neural networks and learning systems Vol. 26; no. 12; pp. 3137 - 3149
• Main Authors: Taherkhani, Aboozar, Belatreche, Ammar, Yuhua Li, Maguire, Liam P.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Action Potentials - physiology; Algorithms; Animals; Biological system modeling; Computational modeling; Delay shift learning; Delays; Humans; Learning; Models, Neurological; Nerve Net - physiology; Neural networks; Neural Networks (Computer); Neurons; Neurons - physiology; spiking neuron; Supervised learning; Synapses - physiology; synaptic delay; Teaching methods; spiking neuron; synaptic delay; Delay shift learning; supervised learning
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2015.2404938

Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.