The purpose of supervised learning for spiking neurons is to emit spikes at desired times. By analyzing the relationship between desired output spike train

actual output spike train

and input spike trains

we argue that the spike selection and calculation of existing algorithms are much complex. By removing the repetitive spike computations which have a negative effect on the overall learning performance

we construct a unit of pair-spike and propose a direct computation method of supervised learning algorithms for spiking neurons. Based on input spikes and the relationship between desired output spike train and actual output spike train

the proposed method utilities every useful input spike selected only once to directly adjust the synaptic weights

which truly reduces the computational cost. Experimental results show that the direct computation method

as a general optimization strategy of supervised learning for spiking neurons

can effectively improve the learning performance of existing algorithms.