The PMNLV model extends single-neuron overdispersion to populations via matrix-normal gain priors, showing shared co-variability highest in V1 and declining along the mouse visual hierarchy.
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Direct SVD solves coupled decompositions; randomized versions with novel balanced subspace selection improve efficiency and apply to face recognition.
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Partitioning Neural Co-Variability
The PMNLV model extends single-neuron overdispersion to populations via matrix-normal gain priors, showing shared co-variability highest in V1 and declining along the mouse visual hierarchy.
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Randomized coupled decompositions
Direct SVD solves coupled decompositions; randomized versions with novel balanced subspace selection improve efficiency and apply to face recognition.