Physiological studies of colour vision have not yet resolved the controversial issue of how chromatic opponency is constructed at a neuronal level. Two competing theories, the cone-selective hypothesis and the random-wiring hypothesis, are currently equivocal to the architecture of the primate retina. In central vision, both schemes are capable of producing colour opponency due to the fact that receptive field centres receive input from a single bipolar cell - the so called 'private line arrangement'. However, in peripheral vision this single-cone input to the receptive field centre is lost, so that any random cone connectivity would result in a predictable reduction in the quality of colour vision. Behavioural studies thus far have indeed suggested a selective loss of chromatic sensitivity in peripheral vision. We investigated chromatic sensitivity as a function of eccentricity for the cardinal chromatic (L/M and S/(L + M)) and achromatic (L + M) pathways, adopting stimulus size as the critical variable. Results show that performance can be equated across the visual field simply by a change of scale (size). In other words, there exists no qualitative loss of chromatic sensitivity across the visual field. Critically, however, the quantitative nature of size dependency for each of the cardinal chromatic and achromatic mechanisms is very specific, reinforcing their independence in terms of anatomy and genetics. Our data provide clear evidence for a physiological model of primate colour vision that retains chromatic quality in peripheral vision, thus supporting the cone-selective hypothesis.
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http://dx.doi.org/10.1113/jphysiol.2005.084855 | DOI Listing |
Taiwan J Ophthalmol
November 2024
Sirindhorn International Institute of Technology, Thammasat University, Bangkok, Thailand.
Recent advances of artificial intelligence (AI) in retinal imaging found its application in two major categories: discriminative and generative AI. For discriminative tasks, conventional convolutional neural networks (CNNs) are still major AI techniques. Vision transformers (ViT), inspired by the transformer architecture in natural language processing, has emerged as useful techniques for discriminating retinal images.
View Article and Find Full Text PDFOphthalmol Sci
November 2024
Liverpool Ocular Oncology Research Group, Department of Eye and Vision Science, Institute of Life Course and Medical Sciences (ILCaMS), University of Liverpool, Liverpool, United Kingdom.
Purpose: Testing the validity of a self-supervised deep learning (DL) model, RETFound, for use on posterior uveal (choroidal) melanoma (UM) and nevus differentiation.
Design: Case-control study.
Subjects: Ultrawidefield fundoscopy images, both color and autofluorescence, were used for this study, obtained from 4255 patients seen at the Liverpool Ocular Oncology Center between 1995 and 2020.
Brain
January 2025
Faculty of Social and Behavioural Sciences, University of Amsterdam, 1001 NK, Amsterdam, The Netherlands.
Mid-level visual processing represents a crucial stage between basic sensory input and higher-level object recognition. The conventional model posits that fundamental visual qualities like color and motion are processed in specialized, retinotopic brain regions (e.g.
View Article and Find Full Text PDFSci Total Environ
January 2025
Molecular Biology, Genetics and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabancı University, Tuzla, Istanbul, Türkiye; USDA/ARS/WRRC, Invasive Species and Pollinator Health Research Unit, Davis, CA 95616, USA. Electronic address:
Neonicotinoid pesticide use has increased around the world despite accumulating evidence of their potential detrimental sub-lethal effects on the behaviour and physiology of bees, and its contribution to the global decline in bee health. Whilst flower colour is considered as one of the most important signals for foraging honey bees (Apis mellifera), the effects of pesticides on colour vision and memory retention in a natural setting remain unknown. We trained free flying honey bee foragers by presenting artificial yellow flower feeder, to an unscented artificial flower patch with 6 different flower colours to investigate if sub-lethal levels of imidacloprid would disrupt the acquired association made between the yellow flower colour from the feeder and food reward.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
Precision depth estimation plays a key role in many applications, including 3D scene reconstruction, virtual reality, autonomous driving and human-computer interaction. Through recent advancements in deep learning technologies, monocular depth estimation, with its simplicity, has surpassed the traditional stereo camera systems, bringing new possibilities in 3D sensing. In this paper, by using a single camera, we propose an end-to-end supervised monocular depth estimation autoencoder, which contains an encoder with a structure with a mixed convolution neural network and vision transformers and an effective adaptive fusion decoder to obtain high-precision depth maps.
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