Physiological model of orientation sensitivity in the visual cortex and its use for image processing

1993 ◽  
Author(s):  
Ilya A. Rybak ◽  
Lubov N. Podladchikova ◽  
N. A. Schevtsova
Keyword(s):  
Image Processing ◽  
Visual Cortex ◽  
Physiological Model ◽  
Orientation Sensitivity

Are neurons in cat posteromedial lateral suprasylvian visual cortex orientation sensitive? Tests with bars and gratings

1995 ◽  
Vol 12 (1) ◽  
pp. 141-151 ◽  
Author(s):  
Yuri Danilov ◽  
Rodney J. Moore ◽  
Von R. King ◽  
Peter D. Spear
Keyword(s):  
Visual Cortex ◽  
Receptive Fields ◽  
Orientation Tuning ◽  
Orientation Sensitivity ◽  
Tuning Curves ◽  
Preferred Direction ◽  
Response Measures ◽  
Direction Of Movement ◽  
The Difference ◽  
Quantitative Response

AbstractThere is controversy in the literature concerning whether or not neurons in the cat's posteromedial lateral suprasylvian (PMLS) visual cortex are orientation selective. Previous studies that have tested cells with simple bar stimuli have found that few, if any, PMLS cells are orientation selective. Conversely, studies that have used repetitive stimuli such as gratings have found that most or all PMLS cells are orientation selective. It is not known whether this difference in results is due to the stimuli used or the laboratories using them. The present experiments were designed to answer this question by testing individual PMLS neurons for orientation sensitivity with both bar and grating stimuli. Using quantitative response measures, we found that most PMLS neurons respond well enough to stationary flashed stimuli to use such stimuli to test for orientation sensitivity. On the basis of these tests, we found that about 85% of the cells with well-defined receptive fields are orientation sensitive to flashed gratings, and a similar percentage are orientation sensitive to flashed bars. About 80% of the cells were orientation sensitive to both types of stimuli. The preferred orientations typically were similar for the two tests, and they were orthogonal to the preferred direction of movement. The strength of the orientation sensitivity (measured as the ratio of discharge to the preferred and nonpreferred orientations) was similar to both types of stimuli. However, the width of the orientation tuning curves was systematically broader to bars than to gratings. Several hypotheses are considered as to why previous studies using bars failed to find evidence for orientation sensitivity. In addition, a mechanism for the difference in orientation tuning to bars and gratings is suggested.

Orientation sensitivity of the cat assessed from evoked potentials: central and peripheral contributions

1985 ◽  
Vol 53 (5) ◽  
pp. 1287-1302 ◽  
Author(s):  
J. S. Tootle ◽  
M. A. Berkley
Keyword(s):  
Visual Cortex ◽  
Evoked Potentials ◽  
Visual Stimulus ◽  
Interstimulus Interval ◽  
Optic Tract ◽  
Stimulus Presentation ◽  
Forward Direction ◽  
Orientation Sensitivity ◽  
Retinal Input ◽  
Band Pass

To estimate contour-orientation sensitivity of the cat and the degree to which precortical processing contributes to such estimates, the amplitude of visually evoked potentials (VEP) recorded from the visual cortex of cats in response to a visual stimulus (S2) presented at various intervals after presentation of another visual stimulus (S1) was measured under several conditions. Recordings were made when both stimuli were presented through one eye (monoptic condition) or when S1 was presented to one eye and S2 to the other (dichoptic condition). In some experiments, simultaneous recordings were made from the optic tract and visual cortex. The stimuli were pairs of sinusoidal gratings with a spatial frequency of 0.5 cycles/deg and of various orientations. Each stimulus was presented by stepping the grating contrast from 0.0 (adapting field) to 0.5 for 50 ms. The intervals between the presentation of the two test stimuli (S1 and S2) was varied from 0 to 1,550 ms, and on different trials the orientation of the S2 grating relative to that of S1 was varied from 0 to 90 degrees. Results showed that under monoptic conditions, the VEP to the second stimulus (S2) was reduced by presentation of the first stimulus (S1) when the interstimulus interval was less than 200 ms, whereas under dichoptic conditions, the response to S2 was reduced with interstimulus intervals less than 75 ms. The response reduction was always in a forward direction (e.g., reduced S2 response), increased in magnitude with decreases in the interstimulus interval, and was larger under monoptic conditions than under dichoptic conditions. The response reduction produced monoptically was orientation selective in that it was greatest when the orientation of S1 and S2 was the same, and it recovered by half when the orientation differed by 6 to 15 degrees (orientation half-band pass). In some cortical recordings, the orientation-selective response reduction was superimposed on a response reduction that was not selective for S2 orientation. Stimultaneous recording in the optic tract also showed a response reduction of S2 response that was not orientation selective, suggesting that precortical neural elements contribute to the cortical VEP. With dichoptic stimulus presentation an orientation-nonspecific response reduction was obtained. We hypothesized that binocular inhibitory effects, resulting from disparate retinal input, produced this surprising finding. The results demonstrate that the VEP recorded at the cortex can be used to estimate orientation sensitivity, but that response interactions in peripheral (precortical) neural elements can contribute to such estimates.

Modeling of local neural networks of the visual cortex and applications to image processing

1991 ◽  
Author(s):  
Ilya A. Rybak ◽  
Natalia A. Shevtsova ◽  
Lubov N. Podladchikova
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Visual Cortex

scholarly journals Brain-Inspired Algorithms for Processing of Visual Data

2021 ◽  
pp. 105-115
Author(s):  
Nicola Strisciuglio ◽  
Nicolai Petkov
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Visual Cortex ◽  
Visual System ◽  
Computational Models ◽  
Hierarchical Organization ◽  
Visual Data ◽  
Convolutional Networks ◽  
Improved Stability ◽  
The Brain

AbstractThe study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing.In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.

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