Brain-Inspired Computing

4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15–19, 2019, Revised Selected Papers

AvKatrin Amunts,Lucio Grandinetti

Häftad, Engelska, 2021

Del 12339 i serien Lecture Notes in Computer Science

448 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

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.

Produktinformation

Utgivningsdatum:2021-07-21
Mått:155 x 235 x 14 mm
Vikt:264 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:159
Förlag:Springer Nature Switzerland AG
ISBN:9783030824266

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Människa – datorinteraktion inom Data och IT
Artificiell intelligens inom Data och IT

Innehållsförteckning

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.-