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Brain-Inspired Computing de Katrin Amunts

Brain-Inspired Computing: Lecture Notes in Computer Science, cartea 10087

Editat de Katrin Amunts, Lucio Grandinetti, Thomas Lippert, Nicolai Petkov
en Limba Engleză Paperback – 11 dec 2016
This book constitutes revised selected papers from the Second International Workshop on Brain-Inspired Computing, BrainComp 2015, held in Cetraro, Italy, in July 2015.
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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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
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

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.