  Spiking neural network (SNN) is interesting due to its strong
bio-plausibility and high energy efficiency. However, its performance is
falling far behind conventional deep neural networks (DNNs). In this paper,
considering a general class of single-spike temporal-coded integrate-and-fire
neurons, we analyze the input-output expressions of both leaky and nonleaky
neurons. We show that SNNs built with leaky neurons suffer from the
overly-nonlinear and overly-complex input-output response, which is the major
reason for their difficult training and low performance. This reason is more
fundamental than the commonly believed problem of nondifferentiable spikes. To
support this claim, we show that SNNs built with nonleaky neurons can have a
less-complex and less-nonlinear input-output response. They can be easily
trained and can have superior performance, which is demonstrated by
experimenting with the SNNs over two popular network intrusion detection
datasets, i.e., the NSL-KDD and the AWID datasets. Our experiment results show
that the proposed SNNs outperform a comprehensive list of DNN models and
classic machine learning models. This paper demonstrates that SNNs can be
promising and competitive in contrast to common beliefs.
