Figure 4.

Unsupervised learning of the MNIST handwritten digits in BindsNET. The DiehlAndCook2015 model implements a simple spike timing-dependent plasticity rule between input and excitatory neuron populations as well as a competitive inhibition mechanism to learn prototypical digit filters from raw data. The DatasetEnvironment wraps the MNIST dataset object so it may be used as a component in the Pipeline. The network is trained on one pass through the 60K-example training data for 350ms each, with state variables (voltages and spikes) reset after each example.