  We present a Spiking Neural Network (SNN) model that incorporates learnable
synaptic delays using Dilated Convolution with Learnable Spacings (DCLS). We
train this model on the Raw Heidelberg Digits keyword spotting benchmark using
Backpropagation Through Time with surrogate gradients. Analysing the
spatio-temporal structure of synaptic interactions in the network we observe
that after training excitation and inhibition are grouped together both in
space and time. To further enhance the efficiency and biological realism of our
model, we implemented a dynamic pruning strategy that combines DEEP R for
connection removal and RigL for connection reintroduction, ensuring that the
network maintains optimal connectivity throughout training. Additionally, we
incorporated Dale's Principle, enforcing each neuron to be exclusively
excitatory or inhibitory -- aligning our model closer to biological neural
networks. We observed that, after training, the spatio-temporal patterns of
excitation and inhibition appeared in the more biologically plausible model as
well. Our research demonstrates the potential of integrating learnable delays,
dynamic pruning, and biological constraints to develop efficient SNN models for
temporal data processing. Furthermore, our results enhance the understanding of
spatio-temporal dynamics in SNNs -- suggesting that the spatio-temporal
features which emerge from training are robust to both pruning and rewiring
processes -- providing a solid foundation for future work in neuromorphic
computing applications.
