  Recent advancements in bio-inspired visual sensing and neuromorphic computing
have led to the development of various highly efficient bio-inspired solutions
with real-world applications. One notable application integrates event-based
cameras with spiking neural networks (SNNs) to process event-based sequences
that are asynchronous and sparse, making them difficult to handle. In this
project, we develop a convolutional spiking neural network (CSNN) architecture
that leverages convolutional operations and recurrent properties of a spiking
neuron to learn the spatial and temporal relations in the ASL-DVS gesture
dataset. The ASL-DVS gesture dataset is a neuromorphic dataset containing hand
gestures when displaying 24 letters (A to Y, excluding J and Z due to the
nature of their symbols) from the American Sign Language (ASL). We performed
classification on a pre-processed subset of the full ASL-DVS dataset to
identify letter signs and achieved 100\% training accuracy. Specifically, this
was achieved by training in the Google Cloud compute platform while using a
learning rate of 0.0005, batch size of 25 (total of 20 batches), 200
iterations, and 10 epochs.
