A Neuromorphic Model for Achromatic and Chromatic Surface Representation of Natural Images

This study develops a neuromorphic model of human lightness perception that is inspired by how the mammalian visual system is designed for this function. It is known that biological visual representations can adapt to a billion-fold change in luminance. How such a system determines absolute lightness under varying illumination conditions to generate a consistent interpretation of surface lightness remains an unsolved problem. Such a process, called "anchoring" of lightness, has properties including articulation, insulation, configuration, and area effects. The model quantitatively simulates such psychophysical lightness data, as well as other data such as discounting the illuminant, the double brilliant illusion, and lightness constancy and contrast effects. The model retina embodies gain control at retinal photoreceptors, and spatial contrast adaptation at the negative feedback circuit between mechanisms that model the inner segment of photoreceptors and interacting horizontal cells. The model can thereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A new anchoring mechanism, called the Blurred-Highest-Luminance-As-White (BHLAW) rule, helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural color images under variable lighting conditions, and is compared with the popular RETINEX model.Air Force Office of Scientific Research (F496201-01-1-0397); Defense Advanced Research Project and the Office of Naval Research (N00014-95-0409, N00014-01-1-0624

Neural Dynamics of 3-D Surface Perception: Figure-Ground Separation and Lightness Perception

This article develops the FACADE theory of three-dimensional (3-D) vision to simulate data concerning how two-dimensional (2-D) pictures give rise to 3-D percepts of occluded and occluding surfaces. The theory suggests how geometrical and contrastive properties of an image can either cooperate or compete when forming the boundary and surface representations that subserve conscious visual percepts. Spatially long-range cooperation and short-range competition work together to separate boundaries of occluding ligures from their occluded neighbors, thereby providing sensitivity to T-junctions without the need to assume that T-junction "detectors" exist. Both boundary and surface representations of occluded objects may be amodaly completed, while the surface representations of unoccluded objects become visible through modal processes. Computer simulations include Bregman-Kanizsa figure-ground separation, Kanizsa stratification, and various lightness percepts, including the Munker-White, Benary cross, and checkerboard percepts.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI 94-01659, IRI 97-20333); Office of Naval Research (N00014-92-J-1309, N00014-95-1-0657

A Neural Model of Surface Perception: Lightness, Anchoring, and Filling-in

This article develops a neural model of how the visual system processes natural images under variable illumination conditions to generate surface lightness percepts. Previous models have clarified how the brain can compute the relative contrast of images from variably illuminate scenes. How the brain determines an absolute lightness scale that "anchors" percepts of surface lightness to us the full dynamic range of neurons remains an unsolved problem. Lightness anchoring properties include articulation, insulation, configuration, and are effects. The model quantatively simulates these and other lightness data such as discounting the illuminant, the double brilliant illusion, lightness constancy and contrast, Mondrian contrast constancy, and the Craik-O'Brien-Cornsweet illusion. The model also clarifies the functional significance for lightness perception of anatomical and neurophysiological data, including gain control at retinal photoreceptors, and spatioal contrast adaptation at the negative feedback circuit between the inner segment of photoreceptors and interacting horizontal cells. The model retina can hereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A later model cortical processing stages, boundary representations gate the filling-in of surface lightness via long-range horizontal connections. Variants of this filling-in mechanism run 100-1000 times faster than diffusion mechanisms of previous biological filling-in models, and shows how filling-in can occur at realistic speeds. A new anchoring mechanism called the Blurred-Highest-Luminance-As-White (BHLAW) rule helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural images under variable lighting conditions.Air Force Office of Scientific Research (F49620-01-1-0397); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); Office of Naval Research (N00014-01-1-0624

A Contrast/Filling-In Model of 3-D Lightness Perception

Wallach's ratio hypothesis states that local luminance ratios clr!termine lightness perception under variable illumination. While local luminance ratios successfully discount gradual variations in illumination (illumination constancy or Type I constancy), they fail to explain lightness constancy in general. Some examples of failures of the ratio hypothesis include effects suggesting the coplanar ratio hypothesis (Gilchrist 1977), "assimilation" effects, and configural effects such as the Benary cross, and White's illusion. The present article extends the Boundary Contour System/Feature Contour System (BCS/FCS) approach to provide an explanation of these effects in terms of a neural model of 3-D lightness perception. Lightness constancy of objects in front of different backgrounds (background constancy or Type II constancy) is used to provide functional constraints to the theory and suggest a contrast negation hypothesis which states that ratio measures between coplanar regions are given more weight in the determination of lightness. Simulations of the model applied to several stimuli including Benary cross and White's illusion show that contrast negation mechanisms modulate illumination constancy mechanisms to extend the explanatory power of the model. The model is also used to devise new stimuli that test theoretical predictions

Quantitative analysis of pixel crosstalk in AMOLED displays

The resolution of organic light-emitting diode (OLED) displays is increasing steadily as these displays are adopted for mobile and virtual reality (VR) devices. This leads to a stronger pixel crosstalk effect, where the neighbors of active pixels unintentionally emit light due to a lateral electric current between the pixels. Recently, the crosstalk was quantified by measuring the current flowing through the common hole transport layer between the neighboring pixels and comparing it to the current through the active pixel diode. The measurements showed that the crosstalk is more crucial for low light levels. In such cases, the intended and parasitic currents are similar. The simulations performed in this study validated these measurement results. By simulations, we quantify the crosstalk current through the diode. The luminous intensity can be calculated with the measured current efficiency of the diodes. For low light levels, the unintended luminance can reach up to 40% of the intended luminance. The luminance due to pixel crosstalk is perceivable by humans. This effect should be considered for OLED displays with resolutions higher than 300 PPI

I. Apples to apples A2A^2: realistic galaxy simulated catalogs and photometric redshift predictions for next-generation surveys

We present new mock catalogues for two of the largest stage-IV next-generation surveys in the optical and infrared: LSST and Euclid, based on an N-body simulation+semi-analytical cone with a posterior modification with \texttt{PhotReal}. This technique modifies the original photometry by using an empirical library of spectral templates to make it more realistic. The reliability of the catalogues is confirmed by comparing the obtained color-magnitude relation, the luminosity and mass function and the angular correlation function with those of real data. Consistent comparisons between the expected photometric redshifts for different surveys are also provided. Very deep near infrared surveys such as Euclid will provide very good performance ($\Delta z/(1+z) \sim 0.025-0.053$) down to $H\sim24$ AB mag and up to $z\sim3$ depending on the optical observations available from the ground whereas extremely deep optical surveys such as LSST will obtain an overall lower photometric redshift resolution ($\Delta z/(1+z) \sim 0.045$) down to $i\sim27.5$ AB mag, being considerably improved ($\Delta z/(1+z) \sim 0.035$) if we restrict the sample down to i$\sim$24 AB mag. Those numbers can be substantially upgraded by selecting a subsample of galaxies with the best quality photometric redshifts. We finally discuss the impact that these surveys will have for the community in terms of photometric redshift legacy. This is the first of a series of papers where we set a framework for comparability between mock catalogues and observations with a particular focus on cluster surveys. The Euclid and LSST mocks are made publicly available in the following link: http://photmocks.obspm.fr/.Comment: accepted in MNRAS. Mocks available in the following link: http://photmocks.obspm.fr

On modifying properties of polymeric melts by nanoscopic particles

We study geometric and energetic factors that partake in modifying properties of polymeric melts via inserting well-dispersed nanoscopic particles (NP). Model systems are polybutadiene melts including 10-150 atom atomic clusters (0.1-1.5% v/v). We tune interactions between chains and particle by van der Waals terms. Using molecular dynamics we study equilibrium fluctuations and dynamical properties at the interface. Effect of bead size and interaction strength both on volume and volumetric fluctuations is manifested in mechanical properties, quantified here by bulk modulus, K. Tuning NP size and non-bonded interactions results in ~15% enhancement in K by addition of a maximum of 1.5% v/v NP.Comment: 25 pages, 7 figure

Visualization of metabolic interaction networks in microbial communities using VisANT 5.0

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues.This work was supported by the National Institutes of Health, R01GM103502-05 to CD, ZH and DS. Partial support was also provided by grants from the Office of Science (BER), U.S. Department of Energy (DE-SC0004962), the Joslin Diabetes Center (Pilot & Feasibility grant P30 DK036836), the Army Research Office under MURI award W911NF-12-1-0390, National Institutes of Health (1RC2GM092602-01, R01GM089978 and 5R01DE024468), NSF (1457695), and Defense Advanced Research Projects Agency Biological Technologies Office (BTO), Program: Biological Robustness In Complex Settings (BRICS), Purchase Request No. HR0011515303, Program Code: TRS-0 Issued by DARPA/CMO under Contract No. HR0011-15-C-0091. Funding for open access charge: National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. (R01GM103502-05 - National Institutes of Health; 1RC2GM092602-01 - National Institutes of Health; R01GM089978 - National Institutes of Health; 5R01DE024468 - National Institutes of Health; DE-SC0004962 - Office of Science (BER), U.S. Department of Energy; P30 DK036836 - Joslin Diabetes Center; W911NF-12-1-0390 - Army Research Office under MURI; 1457695 - NSF; HR0011515303 - Defense Advanced Research Projects Agency Biological Technologies Office (BTO), Program: Biological Robustness In Complex Settings (BRICS); HR0011-15-C-0091 - DARPA/CMO; National Institutes of Health)Published versio

Cortical Dynamics of Boundary Segmentation and Reset: Persistence, Afterimages, and Residual Traces

Using a neural network model of boundary segmentation and reset, Francis, Grossberg, and Mingolla (1994) linked the percept of persistence to the duration of a boundary segmentation after stimulus offset. In particular, the model simulated the decrease of persistence duration with an increase in stimulus duration and luminance. Thc present article reveals further evidence for the neural mechanisms used by the theory. Simulations show that the model reset signals generate orientational afterimages, such as the MacKay effect, when the reset signals can be grouped by a subsequent boundary segmentation that generates illusory contours through them. Simulations also show that the same mechanisms explain properties of residual traces, which increase in duration with stimulus duration and luminance. The model hereby discloses previously unsuspected mechanistic links between data about persistence and afterimages, and helps to clarify the sometimes controversial issues surrounding distinctions between persistence, residual traces, and afterimages.Air Force Office of Scientific Research (F49620-92-J-0499); Office of Naval Research (N00014-91-J-4100, N00014-92-J-4015