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End user AI is trained on large server farms with data collected from theusers. With ever increasing demand for IOT devices, there is a need for deeplearning approaches that can be implemented (at the edge) in an energyefficient manner. In this work we approach this using spiking neural networks.The unsupervised learning technique of spike timing dependent plasticity (STDP)using binary activations are used to extract features from spiking input data.Gradient descent (backpropagation) is used only on the output layer to performthe training for classification. The accuracies obtained for the balancedEMNIST data set compare favorably with other approaches. The effect ofstochastic gradient descent (SGD) approximations on learning capabilities ofour network are also explored.

A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow