  In this work, we propose ReStoCNet, a residual stochastic multilayer
convolutional Spiking Neural Network (SNN) composed of binary kernels, to
reduce the synaptic memory footprint and enhance the computational efficiency
of SNNs for complex pattern recognition tasks. ReStoCNet consists of an input
layer followed by stacked convolutional layers for hierarchical input feature
extraction, pooling layers for dimensionality reduction, and fully-connected
layer for inference. In addition, we introduce residual connections between the
stacked convolutional layers to improve the hierarchical feature learning
capability of deep SNNs. We propose Spike Timing Dependent Plasticity (STDP)
based probabilistic learning algorithm, referred to as Hybrid-STDP (HB-STDP),
incorporating Hebbian and anti-Hebbian learning mechanisms, to train the binary
kernels forming ReStoCNet in a layer-wise unsupervised manner. We demonstrate
the efficacy of ReStoCNet and the presented HB-STDP based unsupervised training
methodology on the MNIST and CIFAR-10 datasets. We show that residual
connections enable the deeper convolutional layers to self-learn useful
high-level input features and mitigate the accuracy loss observed in deep SNNs
devoid of residual connections. The proposed ReStoCNet offers >20x kernel
memory compression compared to full-precision (32-bit) SNN while yielding high
enough classification accuracy on the chosen pattern recognition tasks.
