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Mathematical and Theoretical Neuroscience: Cell, Network and Data Analysis de Giovanni Naldi

Mathematical and Theoretical Neuroscience: Cell, Network and Data Analysis: Springer INdAM Series, cartea 24

Editat de Giovanni Naldi, Thierry Nieus
en Limba Engleză Hardback – 27 mar 2018
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.
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Specificații

ISBN-13: 9783319682969
ISBN-10: 3319682962
Pagini: 226
Ilustrații: IX, 253 p.
Dimensiuni: 155 x 235 mm
Greutate: 5.21 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria Springer INdAM Series

Locul publicării:Cham, Switzerland

Cuprins

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.- 13Measures 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.

Notă biografică

Prof. Giovanni Naldi studied Mathematics at the University of Pavia and at the University of Milan, where he also received his PhD in Applied Mathematics. He is currently a full professor of Numerical Analysis at the University of Milan and the director of the ADAMSS (ADvanced Applied Mathematical and Statistical Sciences) Center at the same University. His research work mainly focuses on the numerical analysis of partial differential equations, wavelet-based methods; multiscale models, non-linear evolution phenomena, biomathematics, and computational neuroscience. He has supervised eight doctoral theses and is the author of more than 60 papers. 

Prof. Thiery Nieus received his PhD in Applied Mathematics at the Department of Mathematics F. Enriques in Milan (Italy). His research focuses on the computations performed by neuronal networks. His work involves the analysis and modeling of multiscale data, ranging from single synapses to population recordings. In September 2016 he joined Marcello Massimini’s laboratory (University of Milan, Italy), working on computational models of the thalamocortical circuit and on complexity measures of TMS/EEG data.

Textul de pe ultima copertă

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.

Caracteristici

Offers a survey on recent hot topics in computational neuroscience Includes state of the art chapters (within a huge literature) Provides recent applications using big data and modelling