Using the Neurorobotics platform to explain global processing in visual crowding
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There have been many advances in the field of reinforcement learning in continuous control problems. Usually, these approaches use deep learning with artificial neural networks for approximation of policies and value functions. In addition, there have been ...
Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacti ...
Background Retinitis pigmentosa (RP) affects 2.5 million people worldwide. Increased identification of causative gene defects and the increasing possibility of treatment necessitates better knowledge of phenotype-genotype correlations to help identify pati ...
Crowding is traditionally thought to occur by local interactions between the target and the neighboring flankers, for example, by pooling neural responses corresponding to both the target and flankers. Accordingly, crowding is thought to occur within a sma ...
Modern technologies enable us to record sequences of online user activity at an unprecedented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating-prediction paradigm, ignoring temporal ...
We propose a data-driven artificial viscosity model for shock capturing in discontinuous Galerkin methods. The proposed model trains a multi-layer feedforward network to map from the element-wise solution to a smoothness indicator, based on which the artif ...
Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...
In crowding, perception of a target deteriorates when neighboring flankers are presented. Flankers close to the fovea deteriorate performance less strongly than flankers presented peripherally, the well-known crowding asymmetries. For example, we presented ...
This contribution gives a review of our results characterizing the propagation in lossy biological tissues. These results were mainly obtained using a simplified spherical model allowing a description in spherical wave decomposition. This model is of cours ...
Background: Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN). New method: T1 ma ...