To decide ''Where to look next ?'' is a central function of the attention system of humans, animals and robots. Control of attention depends on three factors, that is, low-level static and dynamic visual features of the environment (bottom-up), medium-level visual features of proto-objects and the task (top-down). We present a novel integrated computational model that includes all these factors in a coherent architecture based on findings and constraints from the primate visual system. The model combines spatially inhomogeneous processing of static features, spatio-temporal motion features and task-dependent priority control in the form of the first computational implementation of saliency computation as specified by the ''Theory of Visual Attention'' (TVA,[7]). Importantly, static and dynamic processing streams are fused at the level of visual proto-objects, that is, ellipsoidal visual units that have the additional medium-level features of position, size, shape and orientation of the principal axis. Proto-objects serve as input to the TVA process that combines top-down and bottom-up information for computing attentional priorities so that relatively complex search tasks can be implemented. To this end, separately computed static and dynamic proto-objects are filtered and subsequently merged into one combined map of proto-objects. For each proto-object, attentional priorities in the form of attentional weights are computed according to TVA. The target of the next saccade is the center of gravity of the proto-object with the highest weight according to the task. We illustrate the approach by applying it to several real world image sequences and show that it is robust to parameter variations.
Abstract. We implement a novel computational framework for attention that includes recent experimentally derived assumptions on attention which are not covered by standard computational models. To this end, we combine inhomogeneous visual processing, proto-object formation, and parts of TVA (Theory of Visual Attention [2]), a well established computational theory in experimental psychology, which explains a large range of human and monkey data on attention. The first steps of processing employ inhomogeneous processing for the basic visual feature maps. Next, we compute so-called proto-objects by means of blob detection based on these inhomogeneous maps. Our model therefore displays the well known "global-effect" of eye movement control, that is, saccade target landing objects tend to fuse with increasing eccentricity from the center of view. The proto-objects also allow for a straightforward application of TVA and its mechanism to model task-driven selectivity. The final stage of our model consists of an attentional priority map which assigns priority to the proto-objects according to the computations of TVA. This step allows to restrict sophisticated filter computation to the proto-object regions and thereby renders our model computationally efficient by avoiding a complete standard pixel-wise priority computation of bottom-up saliency models.
In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. In order to contribute to this effort, we present GuiltyTargets-COVID-19 (https://guiltytargets-covid.eu/), a machine learning supported web tool to identify novel candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with a lung tissue specific protein-protein interaction network, we demonstrate that GuiltyTargets-COVID-19 is capable of (i) prioritizing meaningful target candidates and assessing their druggability, (ii) unraveling their linkage to known disease mechanisms, (iii) mapping ligands from the ChEMBL database to the identified targets, and (iv) pointing out potential side effects in the case that the mapped ligands correspond to approved drugs. Our example analyses identified 4 potential drug targets from the datasets: AKT3 from both the bulk and single cell RNA-Seq data as well as AKT2, MLKL, and MAPK11 in the single cell experiments. Altogether, we believe that our web tool will facilitate future target identification and drug development for COVID-19, notably in a cell type and tissue specific manner.
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