scholarly journals Comparison of the color selectivity of macaque V4 neurons in different color spaces

2016 ◽  
Vol 116 (5) ◽  
pp. 2163-2172 ◽  
Author(s):  
Takahisa M. Sanada ◽  
Tomoyuki Namima ◽  
Hidehiko Komatsu
Keyword(s):  
Visual Processing ◽  
Color Space ◽  
Limited Range ◽  
Color Information ◽  
Color Representation ◽  
Visual Areas ◽  
Color Selectivity ◽  
Different Color ◽  
V4 Neurons ◽  
Higher Visual Areas

Chromatic selectivity has been studied extensively in various visual areas at different stages of visual processing in the macaque brain. In these studies, color stimuli defined in the Derrington-Krauskopf-Lennie (DKL) color space with a limited range of cone contrast were typically used in early stages, whereas those defined in the Commission Internationale de l'Eclairage (CIE) color space, based on human psychophysical measurements across the gamut of the display, were often used in higher visual areas. To understand how the color information is processed along the visual pathway, it is necessary to compare color selectivity obtained in different areas on a common color space. In the present study, we tested whether the neural color selectivity obtained in DKL space can be predicted from responses obtained in CIE space and whether stimuli with limited cone contrast are sufficient to characterize neural color selectivity. We found that for most V4 neurons, there was a strong correlation between responses measured using the two chromatic coordinate systems, and the color selectivities obtained with the two stimulus sets were comparable. However, for some neurons preferring high- or low-saturation colors, stimuli defined in DKL color space did not adequately capture the neural color selectivity. This is mainly due to the use of stimuli within a limited range of cone contrast. We conclude that regardless of the choice of color space, the sampling of colors across the entire gamut is important to characterize neural color selectivity fully or to compare color selectivities in different areas so as to understand color representation in the visual system.

Study on Reproduction of Color Information of Digital Image Based on Different Color Gamut

2012 ◽  
Vol 430-432 ◽  
pp. 838-841
Author(s):  
Wen Ge Chen
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Color Space ◽  
Mapping Method ◽  
Color Information ◽  
Color Gamut ◽  
Gamut Mapping ◽  
Processing Software ◽  
Image Processing Software ◽  
Different Color

This paper is based on digital image color information reproduction error in a different color gamut,Through the different color gamut mapping method, image processing software Photoshop is used to make experiment and to obtain the corresponding image effect. Using digital presses to print out and use Spectrodensitometer measure the corresponding data.Using Excel software for data processing and analysis, digital image color information of loss situation is obtained in RGB and CMYK color space, It can provide certain basis for control of the color loss.

scholarly journals Representation Sharpening Can Explain Perceptual Priming

2010 ◽  
Vol 22 (5) ◽  
pp. 1312-1332 ◽  
Author(s):  
Samat Moldakarimov ◽  
Maxim Bazhenov ◽  
Terrence J. Sejnowski
Keyword(s):  
Neuronal Activity ◽  
Network Model ◽  
Neural Activity ◽  
Visual Processing ◽  
Physiological Data ◽  
Perceptual Priming ◽  
Wide Range ◽  
Visual Areas ◽  
Higher Visual Areas ◽  
Selection Of

Perceiving and identifying an object is improved by prior exposure to the object. This perceptual priming phenomenon is accompanied by reduced neural activity. But whether suppression of neuronal activity with priming is responsible for the improvement in perception is unclear. To address this problem, we developed a rate-based network model of visual processing. In the model, decreased neural activity following priming was due to stimulus-specific sharpening of representations taking place in the early visual areas. Representation sharpening led to decreased interference of representations in higher visual areas that facilitated selection of one of the competing representations, thereby improving recognition. The model explained a wide range of psychophysical and physiological data observed in priming experiments, including antipriming phenomena, and predicted two functionally distinct stages of visual processing.

Spanning the Face Space

1998 ◽  
Vol 06 (03) ◽  
pp. 265-279 ◽  
Author(s):  
Shimon Edelman
Keyword(s):  
Computational Modeling ◽  
Object Representation ◽  
Receptive Fields ◽  
Computational Approach ◽  
Face Representation ◽  
Comprehensive Theory ◽  
Visual Areas ◽  
The Face ◽  
Higher Visual Areas ◽  
Perceptual Systems

The paper outlines a computational approach to face representation and recognition, inspired by two major features of biological perceptual systems: graded-profile overlapping receptive fields, and object-specific responses in the higher visual areas. This approach, according to which a face is ultimately represented by its similarities to a number of reference faces, led to the development of a comprehensive theory of object representation in biological vision, and to its subsequent psychophysical exploration and computational modeling.

scholarly journals Adaptive Integration in the Visual Cortex by Depressing Recurrent Cortical Circuits

2008 ◽  
Vol 20 (7) ◽  
pp. 1847-1872 ◽  
Author(s):  
Mark C. W. van Rossum ◽  
Matthijs A. A. van der Meer ◽  
Dengke Xiao ◽  
Mike W. Oram
Keyword(s):  
Visual Cortex ◽  
Physiological Data ◽  
High Contrast ◽  
Cortical Circuits ◽  
Response Latencies ◽  
Low Contrast ◽  
Visual Areas ◽  
Higher Visual Areas ◽  
Recurrent Connections

Neurons in the visual cortex receive a large amount of input from recurrent connections, yet the functional role of these connections remains unclear. Here we explore networks with strong recurrence in a computational model and show that short-term depression of the synapses in the recurrent loops implements an adaptive filter. This allows the visual system to respond reliably to deteriorated stimuli yet quickly to high-quality stimuli. For low-contrast stimuli, the model predicts long response latencies, whereas latencies are short for high-contrast stimuli. This is consistent with physiological data showing that in higher visual areas, latencies can increase more than 100 ms at low contrast compared to high contrast. Moreover, when presented with briefly flashed stimuli, the model predicts stereotypical responses that outlast the stimulus, again consistent with physiological findings. The adaptive properties of the model suggest that the abundant recurrent connections found in visual cortex serve to adapt the network's time constant in accordance with the stimulus and normalizes neuronal signals such that processing is as fast as possible while maintaining reliability.

A Stereoscopic Look at Visual Cortex

2005 ◽  
Vol 93 (4) ◽  
pp. 1823-1826 ◽  
Author(s):  
Peter Neri
Keyword(s):  
Visual Cortex ◽  
Perceptual Experience ◽  
Binocular Disparity ◽  
Advanced Stage ◽  
Single Neurons ◽  
Previous Evidence ◽  
Mt Neurons ◽  
Visual Areas ◽  
V4 Neurons ◽  
New Framework

Three recent studies offer new insights into the way visual cortex handles binocular disparity signals. Two of these studies recorded from single neurons in two different visual areas of the monkey brain, one (V5/MT) in dorsal and one (V4) in ventral cortex. While V5/MT neurons respond similarly to neurons in primary visual cortex (V1), V4 neurons appear to reflect a more advanced stage in the analysis of retinal disparity, closer to the perceptual experience of stereoscopic depth. Both studies are consistent with a third study using fMRI to address similar questions in humans. Together with previous evidence, these results suggest a new framework for understanding stereoscopic processing based on the separation between ventral and dorsal streams in visual cortex.

scholarly journals Unique spatial integration in mouse primary visual cortex and higher visual areas

2019 ◽  
Author(s):  
Kevin A. Murgas ◽  
Ashley M. Wilson ◽  
Valerie Michael ◽  
Lindsey L. Glickfeld
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Spatial Information ◽  
Spatial Scales ◽  
Spatial Integration ◽  
Global Features ◽  
Wide Range ◽  
Visual Areas ◽  
Higher Visual Areas

AbstractNeurons in the visual system integrate over a wide range of spatial scales. This diversity is thought to enable both local and global computations. To understand how spatial information is encoded across the mouse visual system, we use two-photon imaging to measure receptive fields in primary visual cortex (V1) and three downstream higher visual areas (HVAs): LM (lateromedial), AL (anterolateral) and PM (posteromedial). We find significantly larger receptive field sizes and less surround suppression in PM than in V1 or the other HVAs. Unlike other visual features studied in this system, specialization of spatial integration in PM cannot be explained by specific projections from V1 to the HVAs. Instead, our data suggests that distinct connectivity within PM may support the area’s unique ability to encode global features of the visual scene, whereas V1, LM and AL may be more specialized for processing local features.

scholarly journals What we talk about when we talk about color

2020 ◽  
Author(s):  
Colin R. Twomey ◽  
Gareth Roberts ◽  
David Brainard ◽  
Joshua B. Plotkin
Keyword(s):  
Visible Light ◽  
Color Space ◽  
Color Perception ◽  
Geographic Location ◽  
Cross Cultural ◽  
Cultural Variation ◽  
Inference Algorithm ◽  
Color Categories ◽  
New Directions ◽  
Different Color

Names for colors vary widely across languages, but color categories are remarkably consistent [1–5]. Shared mechanisms of color perception help explain consistent partitions of visible light into discrete color vocabularies [6–10]. But the mappings from colors to words are not identical across languages, which may reflect communicative needs – how often speakers must refer to objects of different color [11]. Here we quantify the communicative needs of colors in 130 different languages, using a novel inference algorithm. Some regions of color space exhibit 30-fold greater demand for communication than other regions. The regions of greatest demand correlate with the colors of salient objects, including ripe fruits in primate diets. Using the mathematics of compression we predict and empirically test how languages map colors to words, accounting for communicative needs. We also document extensive cultural variation in communicative demands on different regions of color space, which is partly explained by differences in geographic location and local biogeography. This account reconciles opposing theories for universal patterns in color vocabularies, while opening new directions to study cross-cultural variation in the need to communicate different colors.

scholarly journals The relative coding strength of object identity and nonidentity features in human occipito-temporal cortex and convolutional neural networks

2020 ◽  
Author(s):  
Yaoda Xu ◽  
Maryam Vaziri-Pashkam
Keyword(s):  
Visual Processing ◽  
Visual Pathway ◽  
Feature Representation ◽  
Object Categorization ◽  
Object Identity ◽  
Visual Areas ◽  
Ventral Visual Pathway ◽  
Identity Representation ◽  
The Brain ◽  
Early Human

ABSTRACTAny given visual object input is characterized by multiple visual features, such as identity, position and size. Despite the usefulness of identity and nonidentity features in vision and their joint coding throughout the primate ventral visual processing pathway, they have so far been studied relatively independently. Here we document the relative coding strength of object identity and nonidentity features in a brain region and how this may change across the human ventral visual pathway. We examined a total of four nonidentity features, including two Euclidean features (position and size) and two non-Euclidean features (image statistics and spatial frequency content of an image). Overall, identity representation increased and nonidentity feature representation decreased along the ventral visual pathway, with identity outweighed the non-Euclidean features, but not the Euclidean ones, in higher levels of visual processing. A similar analysis was performed in 14 convolutional neural networks (CNNs) pretrained to perform object categorization with varying architecture, depth, and with/without recurrent processing. While the relative coding strength of object identity and nonidentity features in lower CNN layers matched well with that in early human visual areas, the match between higher CNN layers and higher human visual regions were limited. Similar results were obtained regardless of whether a CNN was trained with real-world or stylized object images that emphasized shape representation. Together, by measuring the relative coding strength of object identity and nonidentity features, our approach provided a new tool to characterize feature coding in the human brain and the correspondence between the brain and CNNs.SIGNIFICANCE STATEMENTThis study documented the relative coding strength of object identity compared to four types of nonidentity features along the human ventral visual processing pathway and compared brain responses with those of 14 CNNs pretrained to perform object categorization. Overall, identity representation increased and nonidentity feature representation decreased along the ventral visual pathway, with the coding strength of the different nonidentity features differed at higher levels of visual processing. While feature coding in lower CNN layers matched well with that of early human visual areas, the match between higher CNN layers and higher human visual regions were limited. Our approach provided a new tool to characterize feature coding in the human brain and the correspondence between the brain and CNNs.

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