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Z. Monfared

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#machine learning Preprint Aug 2026

Learning Generalizable Reconstruction of High-Dimensional Neural Dynamics

PCA-DMD provides a scalable operator-theoretic framework that segments LFP recordings into overlapping windows, projects them into a compact PCA space, learns linear Koopman evolution in the latent space, and reconstructs continuous signals through inverse projection and overlap-add aggregation.

Anima Kujur, Z. Monfared · 0 citations