  Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers
significant advantages in terms of energy efficiency, closely mimicking the
behavior of biological neurons. In this work, we delve into the role of skip
connections, a widely used concept in Artificial Neural Networks (ANNs), within
the domain of SNNs with TTFS coding. Our focus is on two distinct types of skip
connection architectures: (1) addition-based skip connections, and (2)
concatenation-based skip connections. We find that addition-based skip
connections introduce an additional delay in terms of spike timing. On the
other hand, concatenation-based skip connections circumvent this delay but
produce time gaps between after-convolution and skip connection paths, thereby
restricting the effective mixing of information from these two paths. To
mitigate these issues, we propose a novel approach involving a learnable delay
for skip connections in the concatenation-based skip connection architecture.
This approach successfully bridges the time gap between the convolutional and
skip branches, facilitating improved information mixing. We conduct experiments
on public datasets including MNIST and Fashion-MNIST, illustrating the
advantage of the skip connection in TTFS coding architectures. Additionally, we
demonstrate the applicability of TTFS coding on beyond image recognition tasks
and extend it to scientific machine-learning tasks, broadening the potential
uses of SNNs.
