  Encoding information with precise spike timings using spike-coded neurons has
been shown to be more computationally powerful than rate-coded approaches.
However, most existing supervised learning algorithms for spiking neurons are
complicated and offer poor time complexity. To address these limitations, we
propose a supervised multi-spike learning algorithm which reduces the required
number of training iterations. We achieve this by formulating a large number of
weight updates as a linear constraint satisfaction problem, which can be solved
efficiently. Experimental results show this method offers better efficiency
compared to existing algorithms on the MNIST dataset. Additionally, we provide
experimental results on the classification capacity of the LIF neuron model,
relative to several parameters of the system.
