Before revision:  The datasets are free and require no additional preprocessing, which renders them broadly applicable to benchmark both software and neuromorphic hardware implementations of spiking neural networks  .
After revision:  The datasets are free and require no additional preprocessing, which renders them broadly applicable to benchmark both software and neuromorphic hardware implementations of spiking neural networks . To accomplish this, we developed a general audio-to-spiking conversion procedure inspired by neurophysiology. Further, we applied this conversion to an existing and a novel speech dataset. The latter is the free, high-fidelity, and word-level aligned Heidelberg digit dataset that we created specifically for this study .