Embedding interpretable $\ell_1$-regression into neural networks for uncovering temporal structure in cell imaging

ArXi:2603.02899v2 Announce Type: replace While artificial neural networks excel in unsupervised learning of non-sparse structure, classical statistical regression techniques offer better interpretability, in particular when sparseness is enforced by $\ell_1$ regularization, enabling identification of which factors drive observed dynamics. We investigate how these two types of approaches can be optimally combined, exemplarily considering two-photon calcium imaging data where sparse autoregressive dynamics are to be extracted.