  Modern well-performing approaches to neural decoding are based on machine
learning models such as decision tree ensembles and deep neural networks. The
wide range of algorithms that can be utilized to learn from neural spike
trains, which are essentially time-series data, results in the need for diverse
and challenging benchmarks for neural decoding, similar to the ones in the
fields of computer vision and natural language processing. In this work, we
propose a spike train classification benchmark, based on open-access neural
activity datasets and consisting of several learning tasks such as stimulus
type classification, animal's behavioral state prediction, and neuron type
identification. We demonstrate that an approach based on hand-crafted
time-series feature engineering establishes a strong baseline performing on par
with state-of-the-art deep learning-based models for neural decoding. We
release the code allowing to reproduce the reported results.
