Mathematical and Theoretical Neuroscience: Cell, Network and Data Analysis

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.