Contrast sensitivity functions in autoencoders.

Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images. Here we reassess the relevance of low-level vision tasks in the explanation of the contrast sensitivity functions (CSFs) in light of 1) the current trend of using artificial neural networks for studying vision, and 2) the current knowledge of retinal image representations. As a first contribution, we show that a very popular type of convolutional neural networks (CNNs), called autoencoders, may develop human-like CSFs in the spatiotemporal and chromatic dimensions when trained to perform some basic low-level vision tasks (like retinal noise and optical blur removal), but not others (like chromatic) adaptation or pure reconstruction after simple bottlenecks). As an illustrative example, the best CNN (in the considered set of simple architectures for enhancement of the retinal signal) reproduces the CSFs with a root mean square error of 11% of the maximum sensitivity. As a second contribution, we provide experimental evidence of the fact that, for some functional goals (at low abstraction level), deeper CNNs that are better in reaching the quantitative goal are actually worse in replicating human-like phenomena (such as the CSFs). This low-level result (for the explored networks) is not necessarily in contradiction with other works that report advantages of deeper nets in modeling higher level vision goals. However, in line with a growing body of literature, our results suggests another word of caution about CNNs in vision science because the use of simplified units or unrealistic architectures in goal optimization may be a limitation for the modeling and understanding of human vision.

On the synthesis of visual illusions using deep generative models.

Visual illusions expand our understanding of the visual system by imposing constraints in the models in two different ways: i) visual illusions for humans should induce equivalent illusions in the model, and ii) illusions synthesized from the model should be compelling for human viewers too. These constraints are alternative strategies to find good vision models. Following the first research strategy, recent studies have shown that artificial neural network architectures also have human-like illusory percepts when stimulated with classical hand-crafted stimuli designed to fool humans. In this work we focus on the second (less explored) strategy: we propose a framework to synthesize new visual illusions using the optimization abilities of current automatic differentiation techniques. The proposed framework can be used with classical vision models as well as with more recent artificial neural network architectures. This framework, validated by psychophysical experiments, can be used to study the difference between a vision model and the actual human perception and to optimize the vision model to decrease this difference.

Evidence for the intrinsically nonlinear nature of receptive fields in vision.

The responses of visual neurons, as well as visual perception phenomena in general, are highly nonlinear functions of the visual input, while most vision models are grounded on the notion of a linear receptive field (RF). The linear RF has a number of inherent problems: it changes with the input, it presupposes a set of basis functions for the visual system, and it conflicts with recent studies on dendritic computations. Here we propose to model the RF in a nonlinear manner, introducing the intrinsically nonlinear receptive field (INRF). Apart from being more physiologically plausible and embodying the efficient representation principle, the INRF has a key property of wide-ranging implications: for several vision science phenomena where a linear RF must vary with the input in order to predict responses, the INRF can remain constant under different stimuli. We also prove that Artificial Neural Networks with INRF modules instead of linear filters have a remarkably improved performance and better emulate basic human perception. Our results suggest a change of paradigm for vision science as well as for artificial intelligence.

Enfermedad de Heck en niño nativo amazónico / Heck disease in native amazonian child

La enfermedad de Heck o hiperplasia epitelial focal es una enfermedad que afecta la mucosa oral de niños y adolescentes principalmente, caracterizada por presentar pápulas que tienden a confluir y formar lesiones papilomatosas, cuyo origen está asociado al virus del papiloma humano. Se presenta en la comunidad Kukush-Yamanunca, parroquia Limoncocha, ciudad de Shushufindi, provincia de Sucumbíos en la Amazonia del Ecuador, el caso de un infante indígena, etnia Shuar, de 9 años de edad, quien acude al puesto de salud Yamanunca por presentar desde hace aproximadamente 10 meses de evolución lesiones papilomatosas en cavidad oral y lengua que corresponden clínicamente con hiperplasia epitelial focal. El conocimiento de las características clínicas y factores de riesgo son pilares importantes para el diagnóstico clínico de esta afección.

Heck's disease or focal epithelial hyperplasia is a disease that affects the oral mucosa of children and adolescents mainly, characterized by presenting papules that tend to converge and form papillomatous lesions, whose origin is associated with the humanpapillomavirus. It is presented in the Kukush-Yamanunca community, Limoncocha parish, Shushufindi city, Sucumbios province in the Amazon of Ecuador, the case of an indigenous infant, 9-year-old Shuar ethnicity, who comes to Yamanunca health post for presenting Approximately 10 months of evolution papillomatous lesions in oral cavity and tongue that correspond clinically with focal epithelial hyperplasia. The knowledge of the clinical characteristics and risk factors are important pillars for the clinical diagnosis of this condition