Neural networks based on temporal encoding with single spikes are biologically more realistic models as experimental evidence suggests that biological neural systems use the exact time of action potentials to encode information.  Moreover, it has been demonstrated that networks of spiking neurons are computationally more powerful than sigmoidal neurons.  In order to reach the computational power of spiking neurons, efficient learning algorithms must be used.  This presentation explores the available supervised learning methods in artificial and spiking neural networks