Topic: interpretable response dimensions

A central goal of sensory neuroscience is to build parsimonious computational models that can both predict neural responses to natural stimuli and reveal interpretable functional organization in the brain. Statistical “component” models can learn interpretable, low-dimensional structure across different brain regions and subjects, but lack an explicit “encoding model” that links these components to the stimuli that drive them, and thus cannot generate predictions for new stimuli or generalize across different experiments. The predictive power of sensory encoding models has improved substantial