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About this paper
Sacha Sokoloski, Ruben Coen-Cagli
The authors introduce a context-dependent Poisson mixture model (CMP) for representing correlated spike counts in macaque visual cortex. A latent component selects a response pattern, with independent Poisson counts within each component and stimulus-dependent mixture weights. Across eight V1 datasets, cross-validation selected 3–5 components, improving held-out fit over the independent-Poisson baseline; this supports the result only in these recordings.
Summary by alphaXiv.
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