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Brain-Inspired Computing de Katrin Amunts

Brain-Inspired Computing: Lecture Notes in Computer Science, cartea 12339

Editat de Katrin Amunts, Lucio Grandinetti, Thomas Lippert, Nicolai Petkov
en Limba Engleză Paperback – 21 iul 2021
This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019.
The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures.
 
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Specificații

ISBN-13: 9783030824266
ISBN-10: 3030824268
Pagini: 168
Ilustrații: VIII, 159 p. 48 illus., 32 illus. in color.
Dimensiuni: 155 x 235 x 10 mm
Greutate: 0.27 kg
Ediția:1st edition 2021
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Machine Learning and Deep learning approaches in human brain mapping.- A high-resolution model of the human entorhinal cortex in the ‘BigBrain’– use case for machine learning and 3D analyses.- Deep learning-supported cytoarchitectonic mapping of the human lateral geniculate body in the BigBrain.- Brain modelling and simulation.- Computational modelling of cerebellar magnetic stimulation: the effect of washout?.- Usage and scaling of an open-source spiking multi-area model of the monkey cortex.- Exascale compute and data infrastructures for neuroscience and applications.- Modular supercomputing for neuroscience.- Fenix: Distributed e-Infrastructure Services for EBRAINS.- Independent component analysis for noise and artifact removal in three-dimensional Polarized Light Imaging.- Exascale artificial and natural neural architectures.- Brain-inspired algorithms for processing of visual data.- An hybrid attention-based system for the prediction of facial attributes.- The statistical physics of learning revisited: Typical learning curves in model scenarios.- Emotion mining: from unimodal to multimodal approaches.-