  The advantage of spiking neural networks (SNNs) over their predecessors is
their ability to spike, enabling them to use spike timing for coding and
efficient computing. A neuromorphic dataset should allow a neuromorphic
algorithm to clearly show that a SNN is able to perform better on the dataset
than an ANN. We have analyzed both N-MNIST and N-Caltech101 along these lines,
but focus our study on N-MNIST. First we evaluate if additional information is
encoded in the time domain in a neuromoprhic dataset. We show that an ANN
trained with backpropagation on frame based versions of N-MNIST and
N-Caltech101 images achieve 99.23% and 78.01% accuracy. These are the best
classification accuracies obtained on these datasets to date. Second we present
the first unsupervised SNN to be trained on N-MNIST and demonstrate results of
91.78%. We also use this SNN for further experiments on N-MNIST to show that
rate based SNNs perform better, and precise spike timings are not important in
N-MNIST. N-MNIST does not, therefore, highlight the unique ability of SNNs. The
conclusion of this study opens an important question in neuromorphic
engineering - what, then, constitutes a good neuromorphic dataset?

Neural and Evolutionary Computing