Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity

Ziyu Lu, Anna J. Li, Alexander Ladd, Pascha Matveev, Aditya Deole, Eric Todd SheaBrown, J. Nathan Kutz, Nicholas A. Steinmetz

TL;DR: We benchmarked 14 probabilistic forecasting models on spontaneous neural activity, revealing the horizon of informative prediction with current methods.

Abstract:

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.

Length: short paper (up to 4 pages)

Domain: methods

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Submission Number: 34