Spiking neural networks are shown to be suitable tools for the processing of spatio-temporal information.However

due to their intricately discontinuous and implicit nonlinear mechanisms

the formulation of efficient supervised learning algorithms for spiking neural networks is difficult

which is an important problem in the research area.In this paper

we introduce the general framework of supervised learning algorithms for spiking neural networks

and analyze their performance evaluations including spike trains learning ability

offline and online processing ability

the locality of learning mechanism and the applicability to network structure.Furthermore

we survey the advance of the research on supervised learning algorithms

which can be divided into three categories according to their differences:gradient descent rule

synaptic plasticity rule

and spike trains convolution rule.Finally

we discuss the advantages and disadvantages of these algorithms

and prospect the problems in current research and some future research directions in this area.