对比八种深度模型，发现最佳可预测鼠脑神经活动1.5秒
Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity
- 用八种概率性深度模型评估神经活动预测性能
- 最佳模型在1.5秒预测内保持高信息量
- 适合神经工程与脑机接口研究者参考
神经活动预测对理解神经系统和实现闭环控制至关重要。尽管深度学习近期在时间序列预测中取得突破，但其在神经活动预测中的应用仍有限。为此，我们系统评估了八种概率性深度学习模型（包括两个基础模型），这些模型在通用预测基准上表现优异。将它们与四种经典统计模型及两种基线方法，在小鼠皮层宽场成像记录的自发神经活动中进行对比。在不同预测时长下，多个深度学习模型持续优于经典方法，其中最优模型可生成长达1.5秒的有信息量预测。结果为未来神经控制应用提供支持，并开启探索神经活动内在时序结构的新路径。
原文摘要 · Abstract (English)
Neural activity forecasting is central to understanding neural systems and enabling closed-loop control. While deep learning has recently advanced the state-of-the-art in the time series forecasting literature, its application to neural activity forecasting remains limited. To bridge this gap, we systematically evaluated eight probabilistic deep learning models, including two foundation models, that have demonstrated strong performance on general forecasting benchmarks. We compared them against four classical statistical models and two baseline methods on spontaneous neural activity recorded from mouse cortex via widefield imaging. Across prediction horizons, several deep learning models consistently outperformed classical approaches, with the best model producing informative forecasts up to 1.5 seconds into the future. Our findings point toward future control applications and open new avenues for probing the intrinsic temporal structure of neural activity.
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