  End user AI is trained on large server farms with data collected from the
users. With ever increasing demand for IOT devices, there is a need for deep
learning approaches that can be implemented (at the edge) in an energy
efficient 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 perform
the training for classification. The accuracies obtained for the balanced
EMNIST data set compare favorably with other approaches. The effect of
stochastic gradient descent (SGD) approximations on learning capabilities of
our network are also explored.
