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.

翻译：神经活动预测对于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领域取得了显著进展，但其在神经活动预测中的应用仍较为有限。为填补这一空白，我们系统评估了八种概率深度学习模型（包括两种基础模型），这些模型在通用预测基准测试中已展现出优异性能。我们将其与四种经典统计模型及两种基线方法进行比较，测试数据来源于通过宽场成像技术记录的小鼠皮层自发神经活动。在不同预测时间跨度下，多种深度学习模型均持续优于经典方法，其中最优模型能够对未来1.5秒内的神经活动生成具有信息量的预测。我们的研究结果为未来控制应用指明了方向，并为探索神经活动内在时间结构开辟了新途径。