Figure 6.

Accompanying plots for the supervised training of a simple two-layer spiking neural network on the Fashion-MNIST dataset. The set of 10 28 × 28 tiled weights shown in (a) each correspond to a different class of Fashion-MNIST data. The plot of the input neurons' activity in (b) is simply the scaled input data, constant over the simulation length. This network architecture trained with stochastic gradient descent (SGD) achieves 85% test accuracy on this dataset. (A) Weights from the supervised spiking neural network trained on the Fashion-MNIST dataset. Each 28 × 28 region corresponds to the filter responsible for detecting a unique category of data. One can make out the profile of objects depicted in the filters; e.g., shirts, sneakers, and trousers. (B) Real-valued input activity and spikes from the input and output layers of the two-layer network, respectively.