Supporting Data and Software for Event-based computation: Unsupervised elementary motion decomposition

We show that the presented architecture allows for unsupervised learning; that synaptic rewiring enhanced to initialise synapses by drawing from a distribution of delays produces more specialised neurons for the task of motion decomposition; and that a pair of readout neurons is sufficient to correctly classify the input based on the target layer's activity using rank-order encoding, rather than spike-rate encoding. Folder structure: |--- simulation_statistics --> analysis scripts and pre-processed simulation results ---|-- preproc --> pre-processed simulation results |--- synaptogenesis ---|-- moving_bar_simulations --> training and testing results for motion learning phase ---|-- readout_simulations --> training and testing results for readout phase ---|-- spiking_moving_bar_input --> moving bar spiking input used throughout |--- spinnaker_software --> snapshot of SpiNNaker software used to generate the presented results