Topic: Ventral visual stream

This webinar features presentations from SueYeon Chung (New York University) and Srinivas Turaga (HHMI Janelia Research Campus) on theoretical and computational approaches to sensory cognition. Chung introduced a “neural manifold” framework to capture how high-dimensional neural activity is structured into meaningful manifolds reflecting object representations. She demonstrated that manifold geometry—shaped by radius, dimensionality, and correlations—directly governs a population’s capacity for classifying or separating stimuli under nuisance variations. Applying these ideas as a data analysis

Visual textures efficiently represent real-world information that is essential for perceptual tasks like pattern detection, object segmentation and classification. This information appears to be encoded in intermediate areas along the primate ventral visual stream (areas V2-V4), but how textural selectivity emerges at the circuit level, and whether the neural architectures that support texture processing are shared across mammalian species is currently unknown. We addressed these questions in the mouse by first examining the mouse’s perceptual ability to discriminate higher order texture stat

Our ability to recognize objects and natural scenes whether depicted in photographs, cartoons, or just line drawings (a.k.a. visual cues) is remarkable. Here, we investigated the representation of a set of surface boundary shapes rendered in different visual cues by macaque V1 and V2 neurons. Specifically, we investigated whether the geometric structure of the population code could support the formation of a cue-invariant representation of abstract surface boundary concepts. A visual concept refers to an abstract feature, such as a boundary or contour, that remains consistent across different