Mathematical and Theoretical Neuroscience Cell, Network and Data Analysis /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Naldi, Giovanni (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Nieus, Thierry (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Έκδοση: 1st ed. 2017.
• Σειρά: Springer INdAM Series, 24
• Θέματα: Applied mathematics.; Engineering mathematics.; Bioinformatics .; Computational biology .; Statistics .; Applications of Mathematics.; Computer Appl. in Life Sciences.; Statistics for Life Sciences, Medicine, Health Sciences.

 This volume gathers contributions from theoretical, experimental and computational researchers who are working on various topics in theoretical/computational/mathematical neuroscience. The focus is on mathematical modeling, analytical and numerical topics, and statistical analysis in neuroscience with applications. The following subjects are considered: mathematical modelling in Neuroscience, analytical  and numerical topics;  statistical analysis in Neuroscience; Neural Networks; Theoretical Neuroscience. The book is addressed to researchers involved in mathematical models applied to neuroscience. 

 1 Simulating cortical Local Field Potentials and Thalamus dynamic regimes with integrate-and-fire neurons -- 2 Computational modeling as a means to defining neuronal spike pattern behaviors -- 3 Chemotactic guidance of growth cones: a hybrid computational model -- 4 Mathematical Modeling of Cerebellar Granular Layer Neurons and Network Activity: Information Estimation, Population Behaviour and Robotic Abstractions -- 5 Bifurcation analysis of a sparse neural network with cubic topology -- 6 Simultaneous jumps in interacting particle systems: from neuronal networks to a general framework -- 7 Neural fields: Localised states with piece-wise constant interactions -- 8 Mathematical models of visual perception based on cortical architectures -- 9 Mathematical models of visual perception for the analysis of Geometrical optical illusions -- 10 Exergaming for autonomous rehabilitation -- 11 E-infrastructures for neuroscientists: the GAAIN and neuGRID examples -- 12 Nonlinear Time series Analysis -- 13 Measures of spike train synchrony and Directionality -- 14 Space-by-time tensor decomposition of single-trial analysis of neural signals -- 15 Inverse Modeling for MEG/EEG data.