11 research outputs found
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The Role of the Primary Visual Cortex in Higher Level Vision
In the classical feed-forward, modular view of visual processing, the primary visual cortex (area V1) is a module that serves to extract local features such as edges and bars. Representation and recognition of objects are thought to be functions of higher extrastriate cortical areas. This paper presents neurophysiological data that show the later part of V1 neurons’ responses reflecting higher order perceptual computations related to Ullman’s (Cognition 1984;18:97–159) visual routines and Marr’s (Vision NJ: Freeman 1982) full primal sketch, 2Image D sketch and 3D model. Based on theoretical reasoning and the experimental evidence, we propose a possible reinterpretation of the functional role of V1. In this framework, because of V1 neurons’ precise encoding of orientation and spatial information, higher level perceptual computations and representations that involve high resolution details, fine geometry and spatial precision would necessarily involve V1 and be reflected in the later part of its neurons’ activities.Mathematic
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The Role of Attention in Figure-Ground Segregation in Areas V1 and V4 of the Visual Cortex
Boundary assignment in a recurrent network architecture
AbstractWe describe a model and simulations of boundary assignment by cortical neurons, a process that assigns edges to figural image regions, as opposed to the background regions on the other side of the edge. The model is composed of several areas, resembling the hierarchical feedforward–feedback organization of areas in the visual cortex. In each successive area along the hierarchy, the visual image is represented at a coarser resolution. Model neurons tend to assign edges to convex image regions. Because of high spatial resolution, information about convexity is not immediately available to all neurons in lower-level areas. In higher-level areas, however, spatial resolution is low, and convexity is coded more reliably. Feedback connections propagate this information to the high-resolution neurons of lower-level visual areas, making it available at all network levels and at all spatial resolutions. The proposed connection scheme assigns edges faster and more reliable to objects than one with only horizontal connections. The model accounts for both psychophysical and neurophysiological data on figural assignment