Spikebench: An open benchmark for spike train time-series classification

TL;DR

This paper introduces Spikebench, a comprehensive benchmark for classifying neural spike train time-series data, facilitating the evaluation of various machine learning models in neural decoding tasks.

Contribution

It presents a new benchmark dataset and tasks for neural spike train classification, enabling standardized evaluation of machine learning approaches in neural decoding.

Findings

Hand-crafted feature engineering achieves performance comparable to deep learning models.

The benchmark includes diverse tasks like stimulus classification and behavioral prediction.

Code for reproducing results is publicly available.

Abstract

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.