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

arXiv: https://arxiv.org/abs/1810.03855 · HTML: https://mlsanity.com/paper/1810.03855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [Tree-Based Learning on Amperometric Time Series Data Demonstrates High Accuracy for Classification](https://mlsanity.com/paper/2302.02650/) (2023)
- [Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification](https://mlsanity.com/paper/2402.09281/) (2024)

## Abstract (arXiv)

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.