  Spiking neural networks (SNNs) have closer dynamics to the brain than current
deep neural networks. Their low power consumption and sample efficiency make
these networks interesting. Recently, several deep convolutional spiking neural
networks have been proposed. These networks aim to increase biological
plausibility while creating powerful tools to be applied to machine learning
tasks. Here, we suggest a network structure based on previous work to improve
network runtime and accuracy. Improvements to the network include reducing
training iterations to only once, effectively using principal component
analysis (PCA) dimension reduction, weight quantization, timed outputs for
classification, and better hyperparameter tuning. Furthermore, the
preprocessing step is changed to allow the processing of colored images instead
of only black and white to improve accuracy. The proposed structure
fractionalizes runtime and introduces an efficient approach to deep
convolutional SNNs.
