Receptive fields of midget ganglion cells and parvocellular lateral geniculate nucleus (LGN) neurons show color-opponent responses because they receive antagonistic input from the middle- and long-wavelength sensitive cones. It has been controversial as to whether this opponency can derive from random connectivity; if receptive field centers of cells near the fovea are cone-specific due to midget morphology, this would confer some degree of color opponency even with random cone input to the surround. A simple test of this mixed surround hypothesis is to compare spatial frequency tuning curves for luminance gratings and gratings isolating cone input to the receptive field center. If tuning curves for luminance gratings were bandpass, then with the mixed surround hypothesis tuning curves for gratings isolating the receptive field center cone class should also be bandpass, but to a lesser extent than for luminance. Tuning curves for luminance, chromatic, and cone-isolating gratings were measured in macaque retinal ganglion cells and LGN cells. We defined and measured a bandpass index to compare luminance and center cone-isolating tuning curves. Midget retinal ganglion cells and parvocellular LGN cells had bandpass indices between 0.1 and 1 with luminance gratings, but the index was usually near 1 (meaning low-pass tuning) when the receptive field center cone class alone was modulated. This is strong evidence for a considerable degree of cone-specific input to the surround. A fraction of midget and parvocellular cells showed evidence of incomplete specificity. Fitting the data with receptive field models revealed considerable intercell variability, with indications in some cells of a more complex receptive structure than a simple difference of Gaussians model.
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http://dx.doi.org/10.1364/JOSAA.29.00A223 | DOI Listing |
Sci Rep
January 2025
School of Electronics and Information, Xijing University, Xi'an, 710123, China.
To enhance high-frequency perceptual information and texture details in remote sensing images and address the challenges of super-resolution reconstruction algorithms during training, particularly the issue of missing details, this paper proposes an improved remote sensing image super-resolution reconstruction model. The generator network of the model employs multi-scale convolutional kernels to extract image features and utilizes a multi-head self-attention mechanism to dynamically fuse these features, significantly improving the ability to capture both fine details and global information in remote sensing images. Additionally, the model introduces a multi-stage Hybrid Transformer structure, which processes features at different resolutions progressively, from low resolution to high resolution, substantially enhancing reconstruction quality and detail recovery.
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Geneis Beijing Co., Ltd, Beijing 100102, China.
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January 2025
Department of Neurophysiology and Chronobiology, Institute of Zoology and Biomedical Research, Jagiellonian University, 9 Gronostajowa street, 30-387 Kraków, Poland.
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Cell Rep
January 2025
Center for Perceptual Systems, The University of Texas at Austin, Austin, TX 78712, USA; Center for Learning and Memory, The University of Texas at Austin, Austin, TX 78712, USA; Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA. Electronic address:
The visual system adapts to maintain sensitivity and selectivity over a large range of luminance intensities. One way that the retina maintains sensitivity across night and day is by switching between rod and cone photoreceptors, which alters the receptive fields and interneuronal correlations of retinal ganglion cells (RGCs). While these adaptations allow the retina to transmit visual information to the brain across environmental conditions, the code used for that transmission varies.
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