scholarly journals Distinct Feedforward and Feedback Effects of Microstimulation in Visual Cortex Reveal Neural Mechanisms of Texture Segregation

Neuron ◽  
2017 ◽  
Vol 95 (1) ◽  
pp. 209-220.e3 ◽  
Author(s):  
P. Christiaan Klink ◽  
Bruno Dagnino ◽  
Marie-Alice Gariel-Mathis ◽  
Pieter R. Roelfsema
Keyword(s):  
Visual Cortex ◽  
Neural Mechanisms ◽  
Feedback Effects ◽  
Texture Segregation

scholarly journals Superimposed gratings induce diverse response patterns of gamma oscillations in primary visual cortex

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bin Wang ◽  
Chuanliang Han ◽  
Tian Wang ◽  
Weifeng Dai ◽  
Yang Li ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Field Potential ◽  
Gamma Oscillations ◽  
Neural Mechanisms ◽  
Model Parameters ◽  
Response Patterns ◽  
Region Difference ◽  
High Gamma ◽  
Spiking Activity

AbstractStimulus-dependence of gamma oscillations (GAMMA, 30–90 Hz) has not been fully understood, but it is important for revealing neural mechanisms and functions of GAMMA. Here, we recorded spiking activity (MUA) and the local field potential (LFP), driven by a variety of plaids (generated by two superimposed gratings orthogonal to each other and with different contrast combinations), in the primary visual cortex of anesthetized cats. We found two distinct narrow-band GAMMAs in the LFPs and a variety of response patterns to plaids. Similar to MUA, most response patterns showed that the second grating suppressed GAMMAs driven by the first one. However, there is only a weak site-by-site correlation between cross-orientation interactions in GAMMAs and those in MUAs. We developed a normalization model that could unify the response patterns of both GAMMAs and MUAs. Interestingly, compared with MUAs, the GAMMAs demonstrated a wider range of model parameters and more diverse response patterns to plaids. Further analysis revealed that normalization parameters for high GAMMA, but not those for low GAMMA, were significantly correlated with the discrepancy of spatial frequency between stimulus and sites’ preferences. Consistent with these findings, normalization parameters and diversity of high GAMMA exhibited a clear transition trend and region difference between area 17 to 18. Our results show that GAMMAs are also regulated in the form of normalization, but that the neural mechanisms for these normalizations might differ from those of spiking activity. Normalizations in different brain signals could be due to interactions of excitation and inhibitions at multiple stages in the visual system.

Man, Monkey, and Blindsight

1998 ◽  
Vol 4 (4) ◽  
pp. 227-230 ◽  
Author(s):  
Tirin Moore ◽  
Hillary R. Rodman ◽  
Charles G. Gross
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Visual Function ◽  
Forced Choice ◽  
Neural Mechanisms ◽  
Residual Vision ◽  
Choice Testing

The visual function that survives damage to the primary visual cortex (V1) in humans is often unaccompanied by awareness. This type of residual vision, called “blindsight,” has raised considerable interest because it implies a separation of conscious from unconscious vision mechanisms. The monkey visual system has proven to be a useful model in elucidating the possible neural mechanisms of residual vision and blindsight in humans. Clear similarities, however, between the phenomenology of human and monkey residual vision have only recently become evident. This article summarizes parallels between residual vision in monkeys and humans with damage to V1. These parallels Include the tendency of the remaining vision to require forced-choice testing and the fact that more robust residual vision remains when V1 damage is sustained early in life. NEUROSCIENTIST 4:227–230

Different Types of Signal Coupling in the Visual Cortex Related to Neural Mechanisms of Associative Processing and Perception

2004 ◽  
Vol 15 (5) ◽  
pp. 1039-1052 ◽  
Author(s):  
R. Eckhorn ◽  
A.M. Gail ◽  
A. Bruns ◽  
A. Gabriel ◽  
B. Al-Shaikhli ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Neural Mechanisms ◽  
Associative Processing ◽  
Different Types

Two distinct neural mechanisms in early visual cortex determine subsequent visual processing

Cortex ◽  
2014 ◽  
Vol 59 ◽  
pp. 1-11 ◽  
Author(s):  
Christianne Jacobs ◽  
Tom A. de Graaf ◽  
Alexander T. Sack
Keyword(s):  
Visual Cortex ◽  
Visual Processing ◽  
Neural Mechanisms ◽  
Early Visual Cortex

scholarly journals Neural mechanisms of rapid natural scene categorization in human visual cortex

Nature ◽  
2009 ◽  
Vol 460 (7251) ◽  
pp. 94-97 ◽  
Author(s):  
Marius V. Peelen ◽  
Li Fei-Fei ◽  
Sabine Kastner
Keyword(s):  
Visual Cortex ◽  
Neural Mechanisms ◽  
Natural Scene ◽  
Scene Categorization ◽  
Human Visual Cortex

Texture Segregation in the Human Visual Cortex: A Functional MRI Study

2000 ◽  
Vol 83 (4) ◽  
pp. 2453-2457 ◽  
Author(s):  
Sabine Kastner ◽  
Peter De Weerd ◽  
Leslie G. Ungerleider
Keyword(s):  
Visual Cortex ◽  
Functional Mri ◽  
Receptive Fields ◽  
Higher Order ◽  
Checkerboard Pattern ◽  
Texture Segregation ◽  
Human Visual Cortex ◽  
Cell Recording ◽  
Visual Scenes ◽  
Mri Study

The segregation of visual scenes based on contour information is a fundamental process of early vision. Contours can be defined by simple cues, such as luminance, as well as by more complex cues, such as texture. Single-cell recording studies in monkeys suggest that the neural processing of complex contours starts as early as primary visual cortex. Additionally, lesion studies in monkeys indicate an important contribution of higher order areas to these processes. Using functional MRI, we have investigated the level at which neural correlates of texture segregation can be found in the human visual cortex. Activity evoked by line textures, with and without texture-defined boundaries, was compared in five healthy subjects. Areas V1, V2/VP, V4, TEO, and V3A were activated by both kinds of line textures as compared with blank presentations. Textures with boundaries forming a checkerboard pattern, relative to uniform textures, evoked significantly more activity in areas V4, TEO, less reliably in V3A, but not in V1 or V2/VP. These results provide evidence that higher order areas with large receptive fields play an important role in the segregation of visual scenes based on texture-defined boundaries.

Texture segregation is processed by primary visual cortex in man and monkey. Evidence from VEP experiments

1992 ◽  
Vol 32 (5) ◽  
pp. 797-807 ◽  
Author(s):  
Victor A.F. Lamme ◽  
Bob W. Van Dijk ◽  
Henk Spekreijse
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Texture Segregation

Meridional Variation in Visual Acuity of Hooded Rats Reared in a Carpentered Environment

Perception ◽  
1981 ◽  
Vol 10 (4) ◽  
pp. 423-430 ◽  
Author(s):  
Paul Dean ◽  
Phillip Horlock ◽  
Ian M Strachan
Keyword(s):  
Animal Model ◽  
Visual Acuity ◽  
Visual Cortex ◽  
Low Frequency ◽  
Oblique Effect ◽  
Neural Mechanisms ◽  
High Contrast ◽  
Square Wave ◽  
Laboratory Rats ◽  
Good Animal Model

Resolution acuity in people is frequently better for horizontal and vertical gratings than for obliques. An animal model of this oblique effect might be of help in elucidating its underlying neural mechanisms. Rats were chosen because laboratory rats are reared in a ‘carpentered environment’ apparently similar to those proposed to cause the oblique effect in people, and because electrophysiological experiments suggest that orientation selective units in rats' visual cortex may prefer horizontal and vertical stimuli. The acuity of eight laboratory-reared hooded rats was measured with high-contrast horizontal, vertical, and oblique gratings. The animals learned to detect low-frequency square-wave gratings with slightly fewer errors if they were horizontal or vertical than if they were oblique, but the effects of grating orientation on acuity were not significant. Refraction of the rats' eyes gave no evidence of astigmatism. These results suggest that the rat may not be a good animal model for studying the mechanisms that underlie meridional variations in acuity in people, and raise questions concerning both the neural bases of resolution acuity, and the validity of the ‘carpentered environment’ hypothesis.

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