Training of a Spiking Neural Network on spintronics based analog hardware for handwritten digit recognition

We trained Spiking neural network (SNN) using spike time dependent plasticity (STDP)-enabled learning under two different learning schemes in MNIST data set(hand written digit recognition). We showed how the post-neurons need to be far more in number than the output classes for larger data sets in the case of SNN for reasonably high accuracy number. We have also reported the net energy consumed for learning in the spintronic devices and associated transistor-based circuits that enable synaptic functionality for this SNN.