Katrin Amunts, Lucio Grandinetti, Thomas Lippert, Nicolai's Brain-Inspired Computing: Second International Workshop, PDF

By Katrin Amunts, Lucio Grandinetti, Thomas Lippert, Nicolai Petkov

ISBN-10: 331950861X

ISBN-13: 9783319508610

ISBN-10: 3319508628

ISBN-13: 9783319508627

This booklet constitutes revised chosen papers from the second one overseas Workshop on Brain-Inspired Computing, BrainComp 2015, held in Cetraro, Italy, in July 2015.
The 14 papers provided during this quantity have been rigorously reviewed and chosen for inclusion during this booklet. They take care of mind constitution and serve as; computational types and brain-inspired computing tools with useful purposes; excessive functionality computing; and visualization for mind simulations.

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Linear system of equations. The idea of parallelization is to distribute the system of ODEs over the available computation nodes such that each node is solving a contiguous subset of the system. g. dot products, the calculation of norms, and linear sums) over the computation nodes. Each node computes the local part of each vector operation followed by a global MPI reduce operation for those operations where it is needed (see [7] for further details on CVODE). Thus this software conceptually uses one instance of the employed ODE solver and distributes its vector computations across the computation nodes.

A parallel image segmentation algorithm on GPUs. In: Proceedings of the 4th GEOBIA, pp. : Parallel image segmentation using modified Hopfield model. Pattern Recogn. Lett. : Parallel 3D Image segmentation of large data sets on a GPU cluster. , et al. ) ISVC 2009. LNCS, vol. 5876, pp. 960–969. Springer, Heidelberg (2009). : Image segmentation by a parallel, nonparametric histogram based clustering algorithm. Pattern Recogn. : Parallel marker-based image segmentation with watershed transformation.

Manual and automatic repair of displaced tissue pieces. Clockwise from topleft: Original section at 20 µm resolution, manual labeled misaligned/damaged ROIs, manual corrected ROIs, and automatically corrected ROIs using neighboring sections [11]. All processing steps were automatically documented and stored in an xml-file. , those scanned with higher spatial resolution. Processing level (i): Manual and automatic repair. Images of histological sections differ in the presence of artifacts and the severity of the artifacts.

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Brain-Inspired Computing: Second International Workshop, BrainComp 2015, Cetraro, Italy, July 6-10, 2015, Revised Selected Papers by Katrin Amunts, Lucio Grandinetti, Thomas Lippert, Nicolai Petkov


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