Brain-Inspired Computing: Second International Workshop, BrainComp 2015, Cetraro, Italy, July 6-10, 2015, Revised Selected Papers

by Katrin Amunts (Editor), Lucio Grandinetti (Editor), Thomas Lippert (Editor), Nicolai Petkov (Editor)

Overview

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.

Product Details

ISBN-13:	9783319508610
Publisher:	Springer International Publishing
Publication date:	01/15/2017
Series:	Lecture Notes in Computer Science , #10087
Edition description:	1st ed. 2016
Pages:	195
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

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.