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Studies in neural data science = Sta...
~
Workshop on the Preservation of Stability under Discretization ((2001 :)
Studies in neural data science = StartUp Research 2017, Siena, Italy, June 25-27 /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Studies in neural data science/ edited by Antonio Canale ... [et al.].
Reminder of title:
StartUp Research 2017, Siena, Italy, June 25-27 /
other author:
Canale, Antonio.
corporate name:
Workshop on the Preservation of Stability under Discretization
Published:
Cham :Springer International Publishing : : 2018.,
Description:
xi, 156 p. :ill., digital ; : 24 cm.;
Contained By:
Springer eBooks
Subject:
Neurosciences - Congresses. - Mathematical models -
Online resource:
https://doi.org/10.1007/978-3-030-00039-4
ISBN:
9783030000394
Studies in neural data science = StartUp Research 2017, Siena, Italy, June 25-27 /
Studies in neural data science
StartUp Research 2017, Siena, Italy, June 25-27 /[electronic resource] :edited by Antonio Canale ... [et al.]. - Cham :Springer International Publishing :2018. - xi, 156 p. :ill., digital ;24 cm. - Springer proceedings in mathematics & statistics,v.2572194-1009 ;. - Springer proceedings in mathematics & statistics ;v.24..
1 S. Ranciati et al, Understanding Dependency Patterns in Structural and Functional Brain Connectivity through fMRI and DTI Data -- 2 E. Aliverti et al, Hierarchical Graphical Model for Learning Functional Network Determinants -- 3 A. Cabassi et al, Three Testing Perspectives on Connectome Data -- 4 A. Cappozzo et al, An Object Oriented Approach to Multimodal Imaging Data in Neuroscience -- 5 G. Bertarelli et al, Curve Clustering for Brain Functional Activity and Synchronization -- 6 F. Gasperoni and A. Luati, Robust Methods for Detecting Spontaneous Activations in fMRI Data -- 7 A. Caponera et al, Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data -- 8 M. Guindani and M. Vannucci, Challenges in the Analysis of Neuroscience Data.
This volume presents a collection of peer-reviewed contributions arising from StartUp Research: a stimulating research experience in which twenty-eight early-career researchers collaborated with seven senior international professors in order to develop novel statistical methods for complex brain imaging data. During this meeting, which was held on June 25-27, 2017 in Siena (Italy), the research groups focused on recent multimodality imaging datasets measuring brain function and structure, and proposed a wide variety of methods for network analysis, spatial inference, graphical modeling, multiple testing, dynamic inference, data fusion, tensor factorization, object-oriented analysis and others. The results of their studies are gathered here, along with a final contribution by Michele Guindani and Marina Vannucci that opens new research directions in this field. The book offers a valuable resource for all researchers in Data Science and Neuroscience who are interested in the promising intersections of these two fundamental disciplines.
ISBN: 9783030000394
Standard No.: 10.1007/978-3-030-00039-4doiSubjects--Topical Terms:
1211846
Neurosciences
--Mathematical models--Congresses.
LC Class. No.: QP351 / .S783 2017
Dewey Class. No.: 612.8233
Studies in neural data science = StartUp Research 2017, Siena, Italy, June 25-27 /
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1 S. Ranciati et al, Understanding Dependency Patterns in Structural and Functional Brain Connectivity through fMRI and DTI Data -- 2 E. Aliverti et al, Hierarchical Graphical Model for Learning Functional Network Determinants -- 3 A. Cabassi et al, Three Testing Perspectives on Connectome Data -- 4 A. Cappozzo et al, An Object Oriented Approach to Multimodal Imaging Data in Neuroscience -- 5 G. Bertarelli et al, Curve Clustering for Brain Functional Activity and Synchronization -- 6 F. Gasperoni and A. Luati, Robust Methods for Detecting Spontaneous Activations in fMRI Data -- 7 A. Caponera et al, Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data -- 8 M. Guindani and M. Vannucci, Challenges in the Analysis of Neuroscience Data.
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This volume presents a collection of peer-reviewed contributions arising from StartUp Research: a stimulating research experience in which twenty-eight early-career researchers collaborated with seven senior international professors in order to develop novel statistical methods for complex brain imaging data. During this meeting, which was held on June 25-27, 2017 in Siena (Italy), the research groups focused on recent multimodality imaging datasets measuring brain function and structure, and proposed a wide variety of methods for network analysis, spatial inference, graphical modeling, multiple testing, dynamic inference, data fusion, tensor factorization, object-oriented analysis and others. The results of their studies are gathered here, along with a final contribution by Michele Guindani and Marina Vannucci that opens new research directions in this field. The book offers a valuable resource for all researchers in Data Science and Neuroscience who are interested in the promising intersections of these two fundamental disciplines.
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