  We extended the work of proposed activation function, Noisy Softplus, to fit
into training of layered up spiking neural networks (SNNs). Thus, any ANN
employing Noisy Softplus neurons, even of deep architecture, can be trained
simply by the traditional algorithm, for example Back Propagation (BP), and the
trained weights can be directly used in the spiking version of the same network
without any conversion. Furthermore, the training method can be generalised to
other activation units, for instance Rectified Linear Units (ReLU), to train
deep SNNs off-line. This research is crucial to provide an effective approach
for SNN training, and to increase the classification accuracy of SNNs with
biological characteristics and to close the gap between the performance of SNNs
and ANNs.
