Brain-Inspired Computing: Lecture Notes in Computer Science
Editat de Katrin Amunts, Lucio Grandinetti, Thomas Lippert, Nicolai Petkoven Limba Engleză Paperback – 11 dec 2016
The 14 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with brain structure and function; computational models and brain-inspired computing methods with practical applications; high performance computing; and visualization for brain simulations.
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| Paperback (2) | 249.77 lei 3-5 săpt. | +14.35 lei 7-13 zile |
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| Springer – 11 dec 2016 | 316.49 lei 6-8 săpt. |
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Specificații
ISBN-13: 9783319508610
ISBN-10: 331950861X
Pagini: 208
Ilustrații: X, 195 p. 47 illus.
Dimensiuni: 155 x 235 x 12 mm
Greutate: 0.32 kg
Ediția:1st edition 2016
Editura: Springer
Seria Lecture Notes in Computer Science
Locul publicării:Cham, Switzerland
ISBN-10: 331950861X
Pagini: 208
Ilustrații: X, 195 p. 47 illus.
Dimensiuni: 155 x 235 x 12 mm
Greutate: 0.32 kg
Ediția:1st edition 2016
Editura: Springer
Seria Lecture Notes in Computer Science
Locul publicării:Cham, Switzerland
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Cuprins
Human Brainnetome Atlas and its Potential Applications in Brain-inspired Computing.- Workflows for ultra-high resolution 3D models of the human brain on massively parallel supercomputers.- Towards large-scale fiber orientation models of the brain automation and parallelization of a seeded region growing segmentation of high-resolution brain section images.- Including gap junctions into distributed neuronal network simulations.- Designing Workflows for the Reproducible Analysis of Electrophysiological Data.- Finite-Difference Time-Domain Simulation for Three-dimensional Polarized Light Imaging.- Visual Processing in Cortical Architecture from Neuroscience to Neuromorphic Computing.- Bio-inspired filters for audio analysis.- Sophisticated LVQ Classification Models - Beyond Accuracy Optimization.- Classification of FDG-PET Brain Data by Generalized Matrix Relevance LVQ.- A Cephalomorph Real-time Computer.- Towards the Ultimate Display for Neuroscientific Data Analysis.- Sentiment Analysis and Affective Computing: methods and applications.- Deep representations for collaborative robotics.
Caracteristici
Includes supplementary material: sn.pub/extras