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.

翻译：现代高性能神经解码方法主要基于机器学习模型，如决策树集成和深度神经网络。由于能够从本质为时间序列数据的神经峰电位序列中学习的算法范围广泛，因此需要类似于计算机视觉和自然语言处理领域的多样化且具有挑战性的神经解码基准。本研究基于开放获取的神经活动数据集，提出了一种峰电位序列分类基准，包含刺激类型分类、动物行为状态预测和神经元类型识别等多种学习任务。我们证明，基于手工特征工程的时间序列方法能够建立与当前最先进的基于深度学习的神经解码模型性能相当的强基线。我们还发布了用于复现所述结果的代码。