ModelDB in Computational Neuroscience Education - A research tool as interactive educational media

ModelDB's mission is to link computational models and publications, supporting the field of computational neuroscience (CNS) by making model source code readily available. It is continually expanding, and currently contains source code for more than 300 models that cover more than 41 topics. Investigators, educators, and students can use it to obtain working models that reproduce published results and can be modified to test for new domains of applicability. Users can browse ModelDB to survey the field of computational neuroscience, or pursue more focused explorations of specific topics. Here we describe tutorials and initial experiences with ModelDB as an interactive educational tool.

Stereocopic Photography Encounters the Staircase

The staircase is presented as the architectural component that most potently embodies thresholds, boundaries and passages due to its diagonal orientation and essence as an intermediary zone. Connections then are made between the kinesthetic requirements of traversing a staircase and viewing a stereoscopic photograph. From this foundation, the haptic essence of stereoscopic photography is proposed as uniquely qualified medium through which to view a staircase and therefore thresholds, boundaries, and passages within architecture. Analyses of stereoviews of staircases in the Palais de Justice in Brussels, the Library of Congress in Washington, and the Palais Garnier (Opéra) in Paris close the essay.

Bitmap Movement Detection: HDR for Dynamic Scenes

Exposure Fusion and other HDR techniques generate well-exposed images from a bracketed image sequence while reproducing a large dynamic range that far exceeds the dynamic range of a single exposure. Common to all these techniques is the problem that the smallest movements in the captured images generate artefacts (ghosting) that dramatically affect the quality of the final images. This limits the use of HDR and Exposure Fusion techniques because common scenes of interest are usually dynamic. We present a method that adapts Exposure Fusion, as well as standard HDR techniques, to allow for dynamic scene without introducing artefacts. Our method detects clusters of moving pixels within a bracketed exposure sequence with simple binary operations. We show that the proposed technique is able to deal with a large amount of movement in the scene and different movement configurations. The result is a ghost-free and highly detailed exposure fused image at a low computational cost.