  We describe a novel spiking neural network (SNN) for automated, real-time
handwritten digit classification and its implementation on a GP-GPU platform.
Information processing within the network, from feature extraction to
classification is implemented by mimicking the basic aspects of neuronal spike
initiation and propagation in the brain. The feature extraction layer of the
SNN uses fixed synaptic weight maps to extract the key features of the image
and the classifier layer uses the recently developed NormAD approximate
gradient descent based supervised learning algorithm for spiking neural
networks to adjust the synaptic weights. On the standard MNIST database images
of handwritten digits, our network achieves an accuracy of 99.80% on the
training set and 98.06% on the test set, with nearly 7x fewer parameters
compared to the state-of-the-art spiking networks. We further use this network
in a GPU based user-interface system demonstrating real-time SNN simulation to
infer digits written by different users. On a test set of 500 such images, this
real-time platform achieves an accuracy exceeding 97% while making a prediction
within an SNN emulation time of less than 100ms.
