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

The human visual cortex processes visual stimuli hierarchically. Early visual areas (V1, V2) of the ventral visual stream feed crude visual features (like orientation and edges) into later visual areas (V4, lateral occipital (Lat Occ), inferior temporal (IT)) that then encode complex visual features (like object form). Previous studies have reported a difference in fMRI responses between natural and urban landscapes in certain parts of the brain. Here we asked if this distinction in the representation of complex natural and man-made visual stimuli extends to the ventral visual stream and if st

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

Deep neural networks are commonly used as models of the ventral visual stream, but are less explored in audition. Prior work provided examples of audio-trained neural networks that produce good predictions of fMRI responses in auditory cortex, and exhibit correspondence between model stages and brain regions, but left it unclear the extent to which these results would generalize to other audio neural network models. We evaluated brain-model correspondence for a wide range of publicly available high-performing audio neural network models along with a set of models that we trained on four differ

Neurons in intermediate visual cortical areas such as V2 and V4, unlike those in V1, respond preferentially to texture images containing naturalistic image statistics common to natural images. We wondered whether and how representations of these image statistics, which we term “naturalness”, changed during development. We made longitudinal behavioral and neural measurements from 2 infant macaque monkeys at the ages of 6 and 12 mo, using texture stimuli that were parametrically varied in naturalness. We used a novel visual search task to efficiently measure behavioral naturalness sensitivity. W

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