Titlebook: Brain-Inspired Computing; 4th International Wo Katrin Amunts,Lucio Grandinetti,Nicolai Petkov Conference proceedings‘‘‘‘‘‘‘‘ 2021 The Edito

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https://doi.org/10.1007/978-3-642-46504-8e-infrastructure resources. In this article we describe the underlying concepts for an audience of computational science end-users and developers of domain-specific applications, workflows and platforms services. To exemplify the use of Fenix, we will discuss selected use cases demonstrating how bra

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Groundwater and Water Management,esign a system to predict facial attributes embedding a biologically-inspired network architecture, the HMAX. The architecture is tailored to predict attributes with different textural information and conveying different semantic meaning, such as attributes related and unrelated to the subject’s ide

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Richard Brook Cathcart,Viorel Badesculy discussing some deep Learning architectures. Furthermore, the paper outlines the challenges and existing resources to perform a multimodal emotion recognition which may improve performances by combining at least two unimodal approaches. architecture to perform multimodal emotion recognition.

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Deep Learning-Supported Cytoarchitectonic Mapping of the Human Lateral Geniculate Body in the BigBraer of analyzed sections, this requires to employ high-performance computing. Based on the serial section delineations, high-resolution 3D reconstruction was performed at 20 µm isotropic resolution of the BigBrain model. The 3D reconstruction shows the shape of the human LGB and its sublayers for the