A Supervised STDP Based Training Algorithm with Dynamic Threshold Neurons

This paper presents an extension of previous work whereby the Spike Timing Dependant Plasticity (STDP) rule was used to train a two layer Spiking Neural Network (SNN). In that work a supervised training algorithm was developed using an STDP based rule that affected weights both locally and at network level. This work extends the rule to a three layer network with multiple inter-neuron excitatory synaptic connections and associated delays. The network utilises dynamic thresholds to facilitate an association between spatial patterns in the input data and classes. The algorithm is benchmarked using nonlinearly separable classification problems and results show that the three layer network exhibits a significant improvement over the two layer.