  We recently proposed the STiDi-BP algorithm, which avoids backward recursive
gradient computation, for training multi-layer spiking neural networks (SNNs)
with single-spike-based temporal coding. The algorithm employs a linear
approximation to compute the derivative of the spike latency with respect to
the membrane potential and it uses spiking neurons with piecewise linear
postsynaptic potential to reduce the computational cost and the complexity of
neural processing. In this paper, we extend the STiDi-BP algorithm to employ it
in deeper and convolutional architectures. The evaluation results on the image
classification task based on two popular benchmarks, MNIST and Fashion-MNIST
datasets with the accuracies of respectively 99.2% and 92.8%, confirm that this
algorithm has been applicable in deep SNNs. Another issue we consider is the
reduction of memory storage and computational cost. To do so, we consider a
convolutional SNN (CSNN) with two sets of weights: real-valued weights that are
updated in the backward pass and their signs, binary weights, that are employed
in the feedforward process. We evaluate the binary CSNN on two datasets of
MNIST and Fashion-MNIST and obtain acceptable performance with a negligible
accuracy drop with respect to real-valued weights (about $0.6%$ and $0.8%$
drops, respectively).
