  Spiking neural networks (SNNs) are brain-inspired machine learning algorithms
with merits such as biological plausibility and unsupervised learning
capability. Previous works have shown that converting Artificial Neural
Networks (ANNs) into SNNs is a practical and efficient approach for
implementing an SNN. However, the basic principle and theoretical groundwork
are lacking for training a non-accuracy-loss SNN. This paper establishes a
precise mathematical mapping between the biological parameters of the Linear
Leaky-Integrate-and-Fire model (LIF)/SNNs and the parameters of ReLU-AN/Deep
Neural Networks (DNNs). Such mapping relationship is analytically proven under
certain conditions and demonstrated by simulation and real data experiments. It
can serve as the theoretical basis for the potential combination of the
respective merits of the two categories of neural networks.
