An unsupervised learning rule for the pulsed neuron model - the vector quantization of the auditory temporal signals

We propose the unsupervised learning rules and the method of the vector quantization for pulsed neuron models to compress the temporal information per every instantaneous time. In the current neural networks, the unsupervised learning rules are widely employed for the vector quantization, dimensionality reduction, self-organization, etc. For the prospective application of the PN models, it is significant to establish the unsupervised learning rules for the models. In terms of the unsupervised learning rules, we examine the application of Kohonen´s competitive learning algorithm