UID:
almahu_9949468827802882
Format:
VIII, 159 p. 48 illus., 32 illus. in color.
,
online resource.
Edition:
1st ed. 2021.
ISBN:
9783030824273
Series Statement:
Theoretical Computer Science and General Issues, 12339
Content:
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. .
Note:
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 -- .
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030824266
Additional Edition:
Printed edition: ISBN 9783030824280
Language:
English
DOI:
10.1007/978-3-030-82427-3
URL:
https://doi.org/10.1007/978-3-030-82427-3
URL:
https://doi.org/10.1007/978-3-030-82427-3
