The supervised learning rules of the pulsed neuron model-learning of the connection weights and the delay times

We propose supervised learning rules for the pulsed neuron model to configure the parameters of the neuron models automatically. We show that the pulsed neuron model with the learning rules can learn two different features which are the pulse frequencies and the time differences. As the results of the simulation, the learning rules can extract both features by the adjustment of the time constant of the local membrane potential's decay /spl tau/.