Mathematical and Theoretical Neuroscience

1. Front Matter

   Pages i-ix

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2. From Single Neuron Activity to Network Information Processing: Simulating Cortical Local Field Potentials and Thalamus Dynamic Regimes with Integrate-and-Fire Neurons

   • Alberto Mazzoni

   Pages 1-23

3. Computational Modeling as a Means to Defining Neuronal Spike Pattern Behaviors

   • Siva Venkadesh, Giorgio A. Ascoli

   Pages 25-43

4. Chemotactic Guidance of Growth Cones: A Hybrid Computational Model

   • Iolanda Morana Roccasalvo, Silvestro Micera, Pier Nicola Sergi

   Pages 45-59

5. Mathematical Modelling of Cerebellar Granular Layer Neurons and Network Activity: Information Estimation, Population Behaviour and Robotic Abstractions

   • Shyam Diwakar, Chaitanya Nutakki, Sandeep Bodda, Arathi Rajendran, Asha Vijayan, Bipin Nair

   Pages 61-85

6. Bifurcation Analysis of a Sparse Neural Network with Cubic Topology

   • Diego Fasoli, Anna Cattani, Stefano Panzeri

   Pages 87-98

7. Simultaneous Jumps in Interacting Particle Systems: From Neuronal Networks to a General Framework

   • Luisa Andreis, Paolo Dai Pra, Markus Fischer

   Pages 99-110

8. Neural Fields: Localised States with Piece-Wise Constant Interactions

   • Aytül Gökçe, Stephen Coombes, Daniele Avitabile

   Pages 111-121

9. Mathematical Models of Visual Perception Based on Cortical Architectures

   • Marta Favali, Giovanna Citti, Alessandro Sarti

   Pages 123-133

10. Mathematical Models of Visual Perception for the Analysis of Geometrical Optical Illusions

    • Benedetta Franceschiello, Alessandro Sarti, Giovanna Citti

    Pages 135-149

11. Exergaming for Autonomous Rehabilitation

    • Nunzio Alberto Borghese

    Pages 151-160

12. E-Infrastructures for Neuroscientists: The GAAIN and neuGRID Examples

    • Daniele Orlandi, Alberto Redolfi, Jérôme Revillard, David Manset, Stefan Teipel, Giovanni B. Frisoni

    Pages 161-176

13. Theory and Application of Nonlinear Time Series Analysis

    • Angelo Di Garbo

    Pages 177-200

14. Measures of Spike Train Synchrony and Directionality

    • Eero Satuvuori, Irene Malvestio, Thomas Kreuz

    Pages 201-222

15. Space-by-Time Tensor Decomposition for Single-Trial Analysis of Neural Signals

    • Ioannis Delis, Arno Onken, Stefano Panzeri

    Pages 223-237

16. Inverse Modeling for MEG/EEG Data

    • Alberto Sorrentino, Michele Piana

    Pages 239-253

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.

• Computational Neurosciences
• Theoretical Neurosciences
• Biophysics of neuron
• Biological neural network dynamics
• Applications of Mathematics

• Environment Science and Policy, University of Milan, Milan, Italy

  Giovanni Naldi

• Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy

  Thierry Nieus

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.

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