Autoregressive flow matching outperforms baselines in probabilistically predicting short-term parcel-wise BOLD neural activity on fMRI data by conditioning on past dynamics and multimodal sensory input.
Friston, Lee Harrison, and William Penny
2 Pith papers cite this work. Polarity classification is still indexing.
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Hierarchy-informed curricular optimization of heterogeneous whole-brain models enables generalization to new subjects and prediction of behavioral abilities from parameters.
citing papers explorer
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Probabilistic Prediction of Neural Dynamics via Autoregressive Flow Matching
Autoregressive flow matching outperforms baselines in probabilistically predicting short-term parcel-wise BOLD neural activity on fMRI data by conditioning on past dynamics and multimodal sensory input.
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Evolution With Purpose: Hierarchy-Informed Optimization of Whole-Brain Models
Hierarchy-informed curricular optimization of heterogeneous whole-brain models enables generalization to new subjects and prediction of behavioral abilities from parameters.