Functional modules for visual scene segmentation in macaque visual cortex

Segmentation, the computation of object boundaries, is one of the most important steps in intermediate visual processing. Previous studies have reported cells across visual cortex that are modulated by segmentation features, but the functional role of these cells remains unclear. First, it is unclear whether these cells encode segmentation consistently since most studies used only a limited variety of stimulus types. Second, it is unclear whether these cells are organized into specialized modules or instead randomly scattered across the visual cortex: the former would lend credence to a functional role for putative segmentation cells. Here, we used fMRI-guided electrophysiology to systematically characterize the consistency and spatial organization of segmentation-encoding cells across the visual cortex. Using fMRI, we identified a set of patches in V2, V3, V3A, V4, and V4A that were more active for stimuli containing figures compared to ground, regardless of whether figures were define..., See the manuscript \"Functional modules for visual scene segmentation in macaque visual cortex\" for more details.
Excerpt:
All animal procedures used in this study complied with local and NIH guidelines including the US NIH Guide for Care and Use of Laboratory Animals. All experiments were performed with the approval of the Caltech Institutional Animal Care and Use Committee (IACUC).
Cylindrical recording chambers (Crist) were implanted using dental acrylic on the left hemisphere of monkey F, the right hemisphere of monkey A, and the right hemisphere of monkey T. Custom grids were printed and inserted into chambers to record from targets defined by fMRI. Chamber positioning and grid design were planned using the software Planner. Guide tubes were cut and inserted through the grid to extend 2 mm beyond the dura. Single tungsten electrodes (FHC) with 1 MΩ impedance were inserted through guide tubes and used for recording. An oil hydraulic microdrive (Narishige) was used to advance electrod..., The dataset can be loaded into MATLAB using the command load('PNASTsao2023_Dryad.mat'). There are also different packages available in python to load this data format.