A Neural Network Model for Long-Range Contour Diffusion by Visual Cortex

2000 ◽  
pp. 336-342 ◽  
Author(s):  
Stéphane Fischer ◽  
Birgitta Dresp ◽  
Charles Kopp
Keyword(s):  
Neural Network ◽  
Visual Cortex ◽  
Long Range ◽  
Network Model ◽  
Neural Network Model

A neural network model for detecting a planar surface spatially from the optical flow in area MST of the visual cortex

2003 ◽  
Vol 34 (4) ◽  
pp. 46-59 ◽  
Author(s):  
Susumu Kawakami ◽  
Masahiro Matsuoka ◽  
Hiroaki Okamoto ◽  
Sinya Hosogi
Keyword(s):  
Neural Network ◽  
Visual Cortex ◽  
Optical Flow ◽  
Network Model ◽  
Neural Network Model ◽  
Planar Surface

Neural network model of visual cortex for determining surface curvature from images of shaded surfaces

1990 ◽  
Vol 240 (1298) ◽  
pp. 251-278 ◽  
Keyword(s):  
Neural Network ◽  
Visual Cortex ◽  
Network Model ◽  
Neural Network Model ◽  
Learning Algorithm ◽  
Receptive Fields ◽  
Principal Curvatures ◽  
Neuronal Function ◽  
Illumination Direction ◽  
Projective Field

The visual system can extract information about shape from the pattern of light and dark surface shading on an object. Very little is known about how this is accomplished. We have used a learning algorithm to construct a neural network model that computes the principal curvatures and orientation of elliptic paraboloids independently of the illumination direction. Our chief finding is that receptive fields developed by units of such model network are surprisingly similar to some found in the visual cortex. It appears that neurons that can make use of the continuous gradations of shading have receptive fields similar to those previously interpreted as dealing with contours (i. e. ‘bar’ detectors or ‘edge’ detec­tors). This study illustrates the difficulty of deducing neuronal function within a network solely from receptive fields. It is also important to consider the pattern of connections a neuron makes with subsequent stages, which we call the ‘projective field’.

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