S. Amari, Dynamics of pattern formation in lateral-inhibition type neural fields, Biological Cybernetics, vol.13, issue.2, pp.77-87, 1977.
DOI : 10.1098/rstb.1952.0012

M. Bastiani, N. J. Shah, R. Goebel, and A. Roebroeck, Human cortical connectome reconstruction from diffusion weighted MRI: The effect of tractography algorithm, NeuroImage, vol.62, issue.3, 2012.
DOI : 10.1016/j.neuroimage.2012.06.002

R. B. Buxton, K. Uludag, D. J. Dubowitz, and T. T. Liu, Modeling the hemodynamic response to brain activation, NeuroImage, vol.23, 2004.
DOI : 10.1016/j.neuroimage.2004.07.013

J. Cabral, E. Hugues, O. Sporns, and G. Deco, Role of local network oscillations in resting-state functional connectivity, NeuroImage, vol.57, issue.1, 2011.
DOI : 10.1016/j.neuroimage.2011.04.010

S. Coombes, Large-scale neural dynamics: Simple and complex, NeuroImage, vol.52, issue.3, pp.731-739, 2010.
DOI : 10.1016/j.neuroimage.2010.01.045

L. Dabbish, C. Stuart, J. Tsay, and J. Herbsleb, Social coding in GitHub, Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work, CSCW '12, pp.1277-1286, 2012.
DOI : 10.1145/2145204.2145396

O. David and K. J. Friston, A neural mass model for MEG/EEG:, NeuroImage, vol.20, issue.3, 2003.
DOI : 10.1016/j.neuroimage.2003.07.015

O. David, S. J. Kiebel, L. M. Harrison, J. Mattout, J. M. Kilner et al., Dynamic causal modeling of evoked responses in EEG and MEG, NeuroImage, vol.30, issue.4, pp.1255-1272, 2006.
DOI : 10.1016/j.neuroimage.2005.10.045
URL : https://hal.archives-ouvertes.fr/inserm-00388967

D. Groff, D. Neelakanta, P. S. Sudhakar, R. Aalo, and V. , Stochastical aspects of neuronal dynamics: Fokker-Planck approach, Biological Cybernetics, vol.65, issue.2, pp.155-164, 1993.
DOI : 10.1007/978-3-642-96807-5

G. Deco, V. Jirsa, and A. Mcintosh, Emerging concepts for the dynamical organization of resting-state activity in the brain, Nature Reviews Neuroscience, vol.4, issue.1, pp.43-56, 1038.
DOI : 10.3389/neuro.09.048.2009

G. Deco, V. Jirsa, A. Mcintosh, O. Sporns, and R. Kötter, Key role of coupling, delay, and noise in resting brain fluctuations, Proceedings of the National Academy of Sciences, vol.8, issue.5, pp.10302-10307, 2009.
DOI : 10.1136/jamia.2001.0080443

G. Deco, V. Jirsa, P. A. Robinson, M. Breakspear, and K. Friston, The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields, PLoS Computational Biology, vol.355, issue.2, 2008.
DOI : 10.1371/journal.pcbi.1000092.t001

G. Deco, A. Ponce-alvarez, D. Mantini, G. L. Romani, P. Hagmann et al., Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations, Journal of Neuroscience, vol.33, issue.27, pp.11239-11252, 2013.
DOI : 10.1523/JNEUROSCI.1091-13.2013
URL : http://www.jneurosci.org/content/jneuro/33/27/11239.full.pdf

G. Deco, M. Senden, and V. Jirsa, How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model, Frontiers in Computational Neuroscience, vol.6, 2012.
DOI : 10.3389/fncom.2012.00068

C. Eliasmith, T. C. Stewart, X. Choo, T. Bekolay, T. Dewolf et al., A Large-Scale Model of the Functioning Brain, Science, vol.116, issue.1, pp.1202-1205, 2012.
DOI : 10.1093/brain/116.1.243

W. Erlhagen and G. Schöner, Dynamic field theory of movement preparation., Psychological Review, vol.109, issue.3, 2002.
DOI : 10.1037/0033-295X.109.3.545

W. J. Freeman, Mass Action in the Nervous System, 1975.

K. J. Friston, L. Harrison, and W. Penny, Dynamic causal modelling, NeuroImage, vol.19, issue.4, pp.1273-1302, 2003.
DOI : 10.1016/S1053-8119(03)00202-7
URL : https://hal.archives-ouvertes.fr/inserm-00388972

W. Gerstner, Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking, Neural Computation, vol.20, issue.1, pp.43-89, 2000.
DOI : 10.1007/BF00288786

A. Ghosh, Y. Rho, A. Mcintosh, R. Kötter, and V. Jirsa, Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire, PLoS Computational Biology, vol.2, issue.10, 2008.
DOI : 10.1371/journal.pcbi.1000196.s011

D. F. Goodman and R. Brette, The brian simulator, Front. Neurosci, vol.3, 2009.

K. Gorgolewski, C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko et al., Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python, Frontiers in Neuroinformatics, vol.5, 2011.
DOI : 10.3389/fninf.2011.00013

A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier et al., MNE software for processing MEG and EEG data, NeuroImage, vol.86, pp.446-460, 2013.
DOI : 10.1016/j.neuroimage.2013.10.027

A. Gramfort, T. Papadopoulo, E. Olivi, and M. Clerc, OpenMEEG: opensource software for quasistatic bioelectromagnetics, BioMedical Engineering OnLine, vol.9, issue.1, pp.45-55, 2010.
DOI : 10.1186/1475-925X-9-45
URL : https://hal.archives-ouvertes.fr/inria-00467061

J. Guckenheimer, P. P. Holmes, L. Cammoun, X. Gigandet, R. Meuli et al., Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields Mapping the structural core of human cerebral cortex, PLoS Biol, 1983.

M. Hamalainen and J. Sarvas, Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data, IEEE Transactions on Biomedical Engineering, vol.36, issue.2, pp.165-171, 1989.
DOI : 10.1109/10.16463

A. V. Herz, R. Meier, M. P. Nawrot, W. Schiegel, and T. Zito, G-Node: An integrated tool-sharing platform to support cellular and systems neurophysiology in the age of global neuroinformatics, Neural Networks, vol.21, issue.8, pp.1070-1075, 2008.
DOI : 10.1016/j.neunet.2008.05.011

M. L. Hines, C. , and N. T. , Neuron: A Tool for Neuroscientists, The Neuroscientist, vol.19, issue.2, pp.123-135, 2001.
DOI : 10.1016/S0306-4522(98)00665-4

C. J. Honey, O. Sporns, L. Cammoun, X. Gigandet, J. P. Thiran et al., Predicting human resting-state functional connectivity from structural connectivity, Proceedings of the National Academy of Sciences, vol.24, issue.6, pp.2035-2040, 2009.
DOI : 10.1016/j.neuroimage.2008.07.063

B. Jansen, R. , and V. , Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns, Biological Cybernetics, vol.580, issue.4, pp.357-366, 1995.
DOI : 10.1007/978-1-4684-8721-3

V. Jirsa and H. Haken, A derivation of a macroscopic field theory of the brain from the quasi-microscopic neural dynamics, Physica D: Nonlinear Phenomena, vol.99, issue.4, pp.503-526, 1997.
DOI : 10.1016/S0167-2789(96)00166-2

V. Jirsa, K. Jantzen, A. Fuchs, and J. Kelso, Spatiotemporal forward solution of the EEG and MEG using network modeling, IEEE Transactions on Medical Imaging, vol.21, issue.5, pp.493-5041009385, 2002.
DOI : 10.1109/TMI.2002.1009385

V. K. Jirsa and H. Haken, Field Theory of Electromagnetic Brain Activity, Physical Review Letters, vol.89, issue.5, pp.960-963, 1996.
DOI : 10.1016/0025-5564(74)90020-0

P. Klöden, Numerical Solution of Stochastic Differential Equations, 1995.

A. Klöckner, N. Pinto, Y. Lee, B. Catanzaro, P. Ivanov et al., PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation, Parallel Computing, vol.38, issue.3, pp.157-174, 2012.
DOI : 10.1016/j.parco.2011.09.001

B. W. Knight, Dynamics of Encoding in Neuron Populations: Some General Mathematical Features, Neural Computation, vol.6, issue.3, pp.473-518, 2000.
DOI : 10.1007/BF00335237

R. Kötter, Online Retrieval, Processing, and Visualization of Primate Connectivity Data From the CoCoMac Database, Neuroinformatics, vol.2, issue.2, pp.127-144127, 2004.
DOI : 10.1385/NI:2:2:127

Y. Kuramoto, Self-entralnment of a population of coupled non-llnear oscillators, chapter 52, Lectures on Physics: International Symposium on Mathematical Problems in Theoretical Physics, pp.420-430, 1007.

D. T. Liley, D. M. Alexander, J. J. Wright, A. , and M. D. , Alpha rhythm emerges from large-scale networks of realistically coupled multicompartmental model cortical neurons, Network: Computation in Neural Systems, vol.10, issue.1, pp.79-92, 1999.
DOI : 10.1088/0954-898X_10_1_005

R. Mannella, R. Mannella, and V. Palleschi, Integration of stochastic differential equations on a computer Fast and precise algorithm for computer simulation of stochastic differential equations, Int. J. Modern Phys. C Phys. Rev. A, vol.1340, issue.40, pp.1177-1194, 1989.

J. S. Mitchell, D. M. Mount, and C. H. Papadimitriou, The Discrete Geodesic Problem, SIAM Journal on Computing, vol.16, issue.4, pp.647-668, 1987.
DOI : 10.1137/0216045

G. Nolte, The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors, Physics in Medicine and Biology, vol.48, issue.22, pp.3637-3652, 2003.
DOI : 10.1088/0031-9155/48/22/002

A. Omurtag, B. W. Knight, and L. Sirovich, On the simulation of large populations of neurons, Journal of Computational Neuroscience, vol.8, issue.1, pp.51-63, 2000.
DOI : 10.1023/A:1008964915724

F. Pérez and B. E. Granger, IPython: A System for Interactive Scientific Computing, Computing in Science & Engineering, vol.9, issue.3, pp.21-29, 2007.
DOI : 10.1109/MCSE.2007.53

H. Risken, The Fokker-Planck Equation: Methods of Solution and Application, 2nd ed., Journal of Applied Mechanics, vol.58, issue.3, 1996.
DOI : 10.1115/1.2897281

M. Rubinov and O. Sporns, Complex network measures of brain connectivity: Uses and interpretations, NeuroImage, vol.52, issue.3, pp.1059-1069, 2010.
DOI : 10.1016/j.neuroimage.2009.10.003

P. Sanz-leon, S. A. Knock, M. M. Woodman, L. Domide, J. Mersmann et al., The Virtual Brain: a simulator of primate brain network dynamics, Frontiers in Neuroinformatics, vol.7, 2013.
DOI : 10.3389/fninf.2013.00010

J. Sarvas, Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem, Physics in Medicine and Biology, vol.32, issue.1, pp.11-22, 1987.
DOI : 10.1088/0031-9155/32/1/004

A. Spiegler and V. Jirsa, Systematic approximations of neural fields through networks of neural masses in the virtual brain, NeuroImage, vol.83, 2013.
DOI : 10.1016/j.neuroimage.2013.06.018

A. Spiegler, S. J. Kiebel, F. M. Atay, and T. R. Knösche, Bifurcation analysis of neural mass models: Impact of extrinsic inputs and dendritic time constants, NeuroImage, vol.52, issue.3, pp.1041-1058, 2010.
DOI : 10.1016/j.neuroimage.2009.12.081

R. Stefanescu and V. Jirsa, A Low Dimensional Description of Globally Coupled Heterogeneous Neural Networks of Excitatory and Inhibitory Neurons, PLoS Computational Biology, vol.92, issue.11, pp.26-36, 2008.
DOI : 10.1371/journal.pcbi.1000219.s004

R. Stefanescu and V. Jirsa, Reduced representations of heterogeneous mixed neural networks with synaptic coupling, Physical Review E, vol.356, issue.2, 2011.
DOI : 10.1137/030600874

M. L. Steyn-ross, D. A. Steyn-ross, J. W. Sleigh, and D. T. Liley, Theoretical electroencephalogram stationary spectrum for a white-noise-driven cortex: Evidence for a general anesthetic-induced phase transition, Physical Review E, vol.273, issue.27, 1999.
DOI : 10.1126/science.273.5283.1812

S. Strogatz, Nonlinear dynamics and chaos: with applications to physics, biology, chemistry and engineering, 2001.

H. Wilson and J. Cowan, Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons, Biophysical Journal, vol.12, issue.1, pp.1-24, 1972.
DOI : 10.1016/S0006-3495(72)86068-5

H. Wilson and J. Cowan, A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue, Kybernetik, vol.12, issue.2, pp.55-80, 1973.
DOI : 10.3758/BF03210193

K. Wong, W. , and X. , A Recurrent Network Mechanism of Time Integration in Perceptual Decisions, Journal of Neuroscience, vol.26, issue.4, pp.1314-1328, 2006.
DOI : 10.1523/JNEUROSCI.3733-05.2006

L. H. Zetterberg, L. Kristiansson, and K. Mossberg, Performance of a model for a local neuron population, Biological Cybernetics, vol.1, issue.1, pp.15-26, 1978.
DOI : 10.1007/BF00337367