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
Date made available11 Mar 2019
PublisherMendeley Data
Date of data production1 Jan 2019 -


  • SpiNNaker
  • Spiking neural networks
  • computer vision
  • structural plasticity
  • neuromorphic computing

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