Learning and data clustering with an RBF-based spiking neuron network

A spiking neuron is a simplified model of the biological neuron as the input, output, and internal representation of information based on the relative timing of individual spikes, and is closely related to the biological network. We extend the learning algorithms with spiking neurons developed by earlier workers. These algorithms explicitly concerned a single pair of pre- and postsynaptic spikes and cannot be applied to situations involving multiple spikes arriving at the same synapse. The aim of the algorithm presented here is to achieve synaptic plasticity by using relative timing between single pre- and postsynaptic spikes and therefore to improve the performance on large datasets. The learning algorithm is based on spike timing-dependent synaptic plasticity, which uses exact spike timing to optimize the information stream through the neural network as well as to enforce the competition between neurons during unsupervised Hebbian learning. We demonstrate the performance of the proposed spiking neuron model and learning algorithm on clustering and provide a comparative analysis with other state-of-the-art approaches.