  Unsupervised feature extraction algorithms form one of the most important
building blocks in machine learning systems. These algorithms are often adapted
to the event-based domain to perform online learning in neuromorphic hardware.
However, not designed for the purpose, such algorithms typically require
significant simplification during implementation to meet hardware constraints,
creating trade offs with performance. Furthermore, conventional feature
extraction algorithms are not designed to generate useful intermediary signals
which are valuable only in the context of neuromorphic hardware limitations. In
this work a novel event-based feature extraction method is proposed that
focuses on these issues. The algorithm operates via simple adaptive selection
thresholds which allow a simpler implementation of network homeostasis than
previous works by trading off a small amount of information loss in the form of
missed events that fall outside the selection thresholds. The behavior of the
selection thresholds and the output of the network as a whole are shown to
provide uniquely useful signals indicating network weight convergence without
the need to access network weights. A novel heuristic method for network size
selection is proposed which makes use of noise events and their feature
representations. The use of selection thresholds is shown to produce network
activation patterns that predict classification accuracy allowing rapid
evaluation and optimization of system parameters without the need to run
back-end classifiers. The feature extraction method is tested on both the
N-MNIST benchmarking dataset and a dataset of airplanes passing through the
field of view. Multiple configurations with different classifiers are tested
with the results quantifying the resultant performance gains at each processing
stage.
