How Neurospectrum works: differentiable curvature and persistence homology, trained end-to-end
Pseudomanifold · x · 2026-11-09
Neurospectrum thread (9/n) explains the core method: the latent trajectory is treated as a geometric object and summarized via curvature, path signatures, persistent homology (Betti curves), zig-zag persistence, and an RNN. All descriptors are computed differentiably, so representation and prediction are optimized jointly end-to-end — the key design behind beating baselines like ICA, HMM, and dFC.
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