  Spikes are the currency in central nervous systems for information
transmission and processing. They are also believed to play an essential role
in low-power consumption of the biological systems, whose efficiency attracts
increasing attentions to the field of neuromorphic computing. However,
efficient processing and learning of discrete spikes still remains as a
challenging problem. In this paper, we make our contributions towards this
direction. A simplified spiking neuron model is firstly introduced with effects
of both synaptic input and firing output on membrane potential being modeled
with an impulse function. An event-driven scheme is then presented to further
improve the processing efficiency. Based on the neuron model, we propose two
new multi-spike learning rules which demonstrate better performance over other
baselines on various tasks including association, classification, feature
detection. In addition to efficiency, our learning rules demonstrate a high
robustness against strong noise of different types. They can also be
generalized to different spike coding schemes for the classification task, and
notably single neuron is capable of solving multi-category classifications with
our learning rules. In the feature detection task, we re-examine the ability of
unsupervised STDP with its limitations being presented, and find a new
phenomenon of losing selectivity. In contrast, our proposed learning rules can
reliably solve the task over a wide range of conditions without specific
constraints being applied. Moreover, our rules can not only detect features but
also discriminate them. The improved performance of our methods would
contribute to neuromorphic computing as a preferable choice.
