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

本研究表明，所提出的架构可实现无监督学习；通过从延迟分布中抽取以初始化突触的突触重连增强机制，能够生成针对运动分解任务更为专化的神经元；并且，一对读出神经元足以根据目标层的活动，通过秩次编码而非放电率编码，对输入信号进行正确分类。