In　spiking　neural　networks,　signals　are　transferred　by　action　potentials.　The　information　is　encoded　in　the　patterns　of　neuron　activities　or　spikes.　These　features　create　significant　differences　between　spiking　neural　networks　and　classical　neural　networks.　Since　spiking　neural　networks　are　based　on　spiking　neuron　models　that　are　very　close　to　the　biological　neuron　model,　many　of　the　principles　found　in　biological　neuroscience　can　be　used　in　the　networks.　In　this　chapter,　a　number　of　learning　mechanisms　for　spiking　neural　networks　are　introduced.　The　learning　mechanisms　can　be　applied　to　explain　the　behaviours　of　networks　in　the　brain;　and　also　can　be　applied　to　artificial　intelligent　systems　to　process　complex　information　represented　by　biological　stimuli.