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A two-layer spiking neural network (a RealNodes object connected all-to-all with a IFNodes object) is trained with an approximated stochastic gradient descent algorithm using the Fashion-MNIST image dataset. The back-propagation algorithm operates on the summed_inputs to the groups of Nodes, while predictions are made based on the output layer's spiking activity.
