MNIST PyGeNN

Implementation of the paper 'Unsupervised learning of digit recognition using spike-timing-dependent plasticity' by Peter Diehl and Matthew Cook, using the PyGeNN (Python interface of GeNN) SNN framework.

To Do:

• Create LIF neuron, synapse and STDP weight update models
• Create LIF neuron and synapse populations
• Load and prepare MNIST data
• Create Poisson input model and input population with variable frequency
• Write simulation code
• Add training and classification code
• Add lateral inhibition and one vs one connections
• Obtain results and plot accuracies