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In the Outer Plexiform Layer of a retina, a cone pedicle provides synaptic inputs for multiple cone bipolar cell (CBC) subtypes so that each subtype formats a parallelized processing channel to filter visual features from the environment. Due to the diversity of short-term depressions among cone-CBC contacts, these channels have different temporal frequency tunings. Here, we propose a theoretical model based on the hierarchy Linear-Nonlinear-Synapse framework to link the synaptic depression and the neural activities of the cone-CBC circuit. The model successfully captures various frequency tunings of subtype-specialized channels and infers synaptic depression recovery time constants inside circuits. Furthermore, the model can predict frequency-tuning behaviors based on synaptic activities. With the prediction of region-specialized UV cone parallel channels, we suggest the acute zone in the zebrafish retina supports detecting light-off events at high temporal frequencies.

The great evolutionary biologist, Theodosius Dobzhansky, once said, "Nothing in biology makes sense except in the light of evolution." Vision, no doubt, is a poster child for the work of evolution. If it has not already been said, I would humbly add that "Nothing in biology makes sense except in the context of metabolism." Marrying these two thoughts together, when one chooses an animal model for vision research, the ground squirrel jumps out immediately for its unique cone dominant retina, which has evolved for its diurnal lifestyle, and for hibernation—an adaptation to unique metabolic challenges encountered during its winter sojourn. [ABSTRACT FROM AUTHOR]

• We propose to divide image gradients into image contours and textural gradients according to whether they belong to the groups of connected gradients with larger spatial extents or groups of connected gradients limited in local spaces. • We use image contours to encode illumination discontinuities and use textural gradients to encode variations in reflectance. • We design a cortex-like contour extraction algorithm and a retina-inspired textural gradient detection algorithm, thus a biologically plausible variation model for retinex decomposition. • We reveal and verify the possible mechanism for lightness computation in the biological vision system and the dependency of Land's retinex theory on the perception of image gradients and image contours. Retinex theory was first proposed by Land and McCann [1], where retinex is a portmanteau derived from the words of retina and cortex, implying that both the retina and cerebral cortex may participate in the perception of lightness and color. However, there are no recent reports on how the retina and visual cortex perform retinex decomposition. In this paper, we propose a biologically plausible solution to retinex decomposition. We develop an algorithm motivated by the primate's retinal circuit to detect textural gradients, design an algorithm originating from the visual cortex to extract image contours, and thus split image edges into image contours and textural gradients. Then, we establish a variational model for retinex decomposition by using image contours and textural gradients to encode discontinuities in illumination and variations in reflectance, respectively. We also apply the proposed retinex model to low light image enhancement, high dynamic resolution image toning, and color constancy. Experiments show consistent superiority of the proposed algorithm. The code is available at Github. [ABSTRACT FROM AUTHOR]

Perception of light in darkness requires no more than a handful of photons, and this remarkable behavioral performance can be directly linked to a particular retinal circuit—the retinal ON pathway. However, the neural limits of shadow detection in very dim light have remained unresolved. Here, we unravel the neural mechanisms that determine the sensitivity of mice (CBA/CaJ) to light decrements at the lowest light levels by measuring signals from the most sensitive ON and OFF retinal ganglion cell types and by correlating their signals with visually guided behavior. We show that mice can detect shadows when only a few photon absorptions are missing among thousands of rods. Behavioral detection of such "quantal" shadows relies on the retinal OFF pathway and is limited by noise and loss of single-photon signals in retinal processing. Thus, in the dim-light regime, light increments and decrements are encoded separately via the ON and OFF retinal pathways, respectively. [Display omitted] • Mice can see quantal shadows elicited by a few missing photons in thousands of rods • Behavior guided by shadows relies on the most sensitive retinal OFF ganglion cells • The limit for detecting quantal shadows is set by retinal losses and neural noise Westö et al. show that mice can detect quantal shadows—negative contrasts representing a small number of missing photons—at starlight. This remarkable behavior relies on the most sensitive OFF retinal ganglion cells and is limited only by neural noise and the inevitable loss of single photons in the retina. [ABSTRACT FROM AUTHOR]

ABSTRACT Approaching object detector structure has been recently identified in mouse retina, and its neural circuit model has been successfully built. A discrete-type mathematical model describing the retinal structure has been synthesized and implemented on a biologically motivated focal-plane mixed-signal sensor-processor device. The model responses have been analyzed, and the critical parameters have been identified, and their roles were studied. The model has been validated in real dynamic surround of a robotic arm, where the geometrical and the kinematic parameters of the movements were known. From the mathematical and experimental analysis, it has been concluded that this retinal structure has a capacity to generate a last second warning signal to avoid collisions. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]