There's a supervised learning algorithm for SNN that enables a single neuron to learn spike pattern associations of input-output spike sequences at the precise times of spikes. This algorithm is termed SPAN(Spike Pattern Association Neuron). Anyone can build SNN to associate the input to output temporal patterns of desired spike sequences using this SPAN neuron. Here the input, output, and desired spike trains are transformed into analog signals by convolving the spikes with a kernel function. This transformation simplifies the computation of the error signal and, therefore, allows the application of gradient descent to optimize the synaptic weights.