Probabilistic Prediction of Neural Dynamics via Autoregressive Flow Matching
Forecasting neural activity in response to naturalistic stimuli remains a key challenge for understanding brain dynamics and enabling downstream neurotechnological applications. Here, we introduce a generative forecasting framework for modeling neural dynamics based on autoregressive flow matching (AFM). Building on recent advances in transport-based generative modeling, our approach probabilistically predicts neural responses at scale from multimodal sensory input. Specifically, we learn the conditional distribution of future neural activity given past neural dynamics and concurrent sensory input, explicitly modeling neural activity as a temporally evolving process in which future states...
| Authors | Nicole Rogalla, Yuzhen Qin, Mario Senden et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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