Bibliography

Major publications by the team in recent years

1G. Aubert, P. Kornprobst.

Mathematical problems in image processing: partial differential equations and the calculus of variations (Second edition), Applied Mathematical Sciences, Springer-Verlag, 2006, vol. 147.
2B. Cessac.

A discrete time neural network model with spiking neurons II. Dynamics with noise., in: Journal of Mathematical Biology, 2011, vol. 62, n^o 6, p. 863-900. [ DOI : 10.1007/s00285-010-0358-4 ]

http://lanl.arxiv.org/pdf/1002.3275
3B. Cessac, M. Samuelides.

From neuron to neural networks dynamics., in: EPJ Special topics: Topics in Dynamical Neural Networks, 2007, vol. 142, n^o 1, p. 7–88. [ DOI : 10.1140/epjst/e2007-00058-2 ]

http://lanl.arxiv.org/abs/nlin/0609038
4O. Faugeras, J. Touboul, B. Cessac.

A constructive mean field analysis of multi population neural networks with random synaptic weights and stochastic inputs, in: Frontiers in Computational Neuroscience, 2009, vol. 3, n^o 1. [ DOI : 10.3389/neuro.10.001.2010 ]

http://www.frontiersin.org/computational_neuroscience/10.3389/neuro.10/001.2009/abstract
5F. Grimbert, O. Faugeras.

Bifurcation Analysis of Jansen's Neural Mass Model, in: Neural Computation, December 2006, vol. 18, n^o 12, p. 3052–3068.
6E. Tlapale, G. Masson, P. Kornprobst.

Modelling the dynamics of motion integration with a new luminance-gated diffusion mechanism, in: Vision Research, August 2010, vol. 50, n^o 17, p. 1676–1692.

http://dx.doi.org/10.1016/j.visres.2010.05.022
7J. Touboul, O. Faugeras.

The spikes trains probability distributions: a stochastic calculus approach, in: Journal of Physiology, Paris, December 2007, vol. 101/1-3, p. 78–98.
8J. Touboul, O. Faugeras.

First hitting time of Double Integral Processes to curved boundaries, in: Advances in Applied Probability, 2008, vol. 40, n^o 2, p. 501–528.
9J. Touboul.

Bifurcation Analysis of a General Class of Nonlinear Integrate-and-Fire Neurons, in: SIAM Journal on Applied Mathematics, 2008, vol. 68, n^o 4, p. 1045-1079.

http://link.aip.org/link/?SMM/68/1045/1
10A. Wohrer, P. Kornprobst.

Virtual Retina : A biological retina model and simulator, with contrast gain control, in: Journal of Computational Neuroscience, 2009, vol. 26, n^o 2, 219 p, DOI 10.1007/s10827-008-0108-4.

Publications of the year

Doctoral Dissertations and Habilitation Theses

11M. Galtier.

A mathematical approach to unsupervised learning in recurrent neural networks, ParisTech, December 2011.
12H. Rostro-Gonzalez.

Computing with spikes, architecture, properties and implementation of emerging paradigms, EDSTIC, 2011.
13E. Tlapale.

Modelling the dynamics of contextual motion integration in the primate, Université Nice Sophia Antipolis, January 2011.
14J.-C. Vasquez.

Analyzing the neural code, mathematical and computational properties of spiking neural networks, EDSTIC, 2011.
15R. Veltz.

Nonlinear analysis methods in neural field models, Univ Paris Est ED MSTIC, 2011.

Articles in International Peer-Reviewed Journal

16J. Bouecke, E. Tlapale, P. Kornprobst, H. Neumann.

Neural Mechanisms of Motion Detection, Integration, and Segregation: From Biology to Artificial Image Processing Systems, in: EURASIP, special issue on Biologically inspired signal processing: Analysis, algorithms, and applications, 2011, vol. 2011. [ DOI : 10.1155/2011/781561 ]

http://www.hindawi.com/journals/asp/2011/781561.html
17B. Cessac.

A discrete time neural network model with spiking neurons II. Dynamics with noise., in: Journal of Mathematical Biology, 2011, vol. 62, n^o 6, p. 863-900. [ DOI : 10.1007/s00285-010-0358-4 ]

http://lanl.arxiv.org/pdf/1002.3275
18B. Cessac.

Statistics of spike trains in conductance-based neural networks: Rigorous results, in: The Journal of Mathematical Neuroscience, 2011, vol. 1, n^o 8, p. 1-42. [ DOI : 10.1186/2190-8567-1-8 ]

http://www.mathematical-neuroscience.com/content/1/1/8
19P. Chossat, G. Faye, O. Faugeras.

Bifurcations of hyperbolic planforms, in: Journal of Nonlinear Science, August 2011, vol. 21, n^o 4, p. 465–498.

http://www.springerlink.com/content/l6386p2501x14265/fulltext.pdf
20T. Deneux, O. Faugeras, S. Takerkart, G. Masson, I. Vanzetta.

A new variational method for erythrocyte velocity estimation in wide-field imaging in-vivo, in: IEEE TMI, 2011, vol. 30, n^o 8, p. 1527–1545.

http://dx.doi.org/10.1109/TMI.2011.2131151
21G. Faye, P. Chossat, O. Faugeras.

Analysis of a hyperbolic geometric model for visual texture perception, in: The Journal of Mathematical Neuroscience, 2011, vol. 1, n^o 4.
22G. Faye, P. Chossat.

Bifurcation diagrams and heteroclinic networks of octagonal H-planforms, in: Accepted for publication in Journal of Nonlinear Science, 2011.

http://hal.archives-ouvertes.fr/hal-00587900/
23A. Ramirez, M. Rivera, P. Kornprobst, F. Lauze.

Variational multi-valued velocity field estimation for transparent sequences, in: Journal of Mathematical Image and Vision, 2011, vol. 40, n^o 3, p. 285–304. [ DOI : DOI: 10.1007/s10851-011-0260-8 ]

http://www.springerlink.com/content/b8170jl10r87jl10/
24H. Rostro-Gonzalez, B. Cessac, B. Girau, C. Torres-Huitzil.

The role of the asymptotic dynamics in the design of FPGA-based hardware implementations of gIF-type neural networks, in: J. Physiol. Paris, 2011, vol. 105, n^o 1–3, p. 91–97, to appear. [ DOI : 10.1016/j.jphysparis.2011.09.004 ]

http://www.sciencedirect.com/science/article/pii/S0928425711000301
25H. Rostro-Gonzalez, B. Cessac, T. Viéville.

Parameters estimation in spiking neural networks: a reverse-engineering approach, in: J. Neural. Eng., 2011, Accepted.
26J. Touboul, O. Faugeras.

A Markovian event-based framework for stochastic spiking neural networks, in: Journal of Computational Neuroscience, April 2011, vol. 30.

http://www.springerlink.com/content/81736mn03j2221m7/fulltext.pdf
27J. Touboul, F. Wendling, P. Chauvel, O. Faugeras.

Neural Mass Activity, Bifurcations, and Epilepsy, in: Neural Computation, December 2011, vol. 23, n^o 12, p. 3232–3286.
28J.-C. Vasquez, A. Palacios, O. Marre, Michael. J. II. Berry, B. Cessac.

Gibbs distribution analysis of temporal correlation structure on multicell spike trains from retina ganglion cells, in: J. Physiol. Paris, 2011, to appear.

http://arxiv.org/abs/1112.2464
29R. Veltz, O. Faugeras.

Stability of the stationary solutions of neural field equations with propagation delays, in: The Journal of Mathematical Neuroscience, 2011, vol. 1, n^o 1, 1 p.

http://www.mathematical-neuroscience.com/content/1/1/1
30R. Veltz.

An analytical method for computing Hopf bifurcation curves in neural field networks with space-dependent delays, in: Comptes Rendus Mathematique, July 2011, vol. 349, p. 749–752.

Invited Conferences

31B. Cessac.

Comportements émergents dans les réseaux de neurones., in: Berder 2011 Coopérativité et singularité en biologie, avril 2011.

http://www.lptl.jussieu.fr/user/lesne/berder2011
32B. Cessac.

Neural networks: some results about (i) Spike train statistics ; (ii) Linear response theory., in: Mean-Field methods and multiscale analysis in neuronal populations, r, October 2011.

http://www.cirm.univ-mrs.fr/index.html/
33B. Cessac.

Spike trains statistics and Gibbs distributions, in: "Dynamics of Complex Systems" Closing conference, Cergy, Sept 2011.

http://agm.u-cergy.fr/dynamicsofcomplexsystems/finalevent.html
34B. Cessac.

Statistics of action potentials in neural networks: from experiments to mathematics, in: Stochastic Dynamics in Mathematics, Physics and Engineering, Bielefeld 1-4 November, 2011.

http://www.uni-bielefeld.de/
35D. Fasoli, O. Faugeras, J. Touboul.

Chaos propagation in a mean field theory of spiking neural networks, in: Brainscales General Meeting, Barcelona, March 2011.
36O. Faugeras.

Bridging the gaps between micro/meso/macro levels, in: ANR-NSF Workshop, ANR, Paris, France, November 2011.
37O. Faugeras.

Connections between mathematics and neuroscience, in: TAMTAM'11, Sousse, Tunisia, April 2011.
38O. Faugeras.

Mathematics and Neuroscience, in: Computer Vision Day Trip, Universidad de la República, Montevideo, Uruguay, March 2011.
39O. Faugeras.

Mean field methods in neuroscience, in: EPFL lectures, EPFL, Lausanne, Switzerland, June 2011.
40O. Faugeras.

Neural Field models of some aspects of visual perception, in: ANR-NSF Workshop, ANR, Paris, France, November 2011.
41O. Faugeras.

Propagation to chaos and information processing in large assemblies of neurons, in: CNS workshop on Methods of Information Theory in Computational Neuroscience, KTH, Stockholm, Sweden, July 2011.
42O. Faugeras.

Propagation to chaos and information processing in large assemblies of neurons, in: Mathematical Neuroscience minisymposium, Equadiff 2011, Loughborough University, Loughborough/UK, August 2011.
43O. Faugeras.

Two or three things I know about mean-field methods for large assemblies of neurons, in: Workshop on Mean-field methods and multiscale analysis of neuronal populations, CIRM, Luminy, France, October 2011.
44O. Faugeras, R. Veltz.

Constraining the design and operation of neural field models from basic principles, in: Workshop on Spatio-temporal evolution equations and neural fields, October 2011.
45G. Faye, P. Chossat, O. Faugeras.

Overview of the structure tensor model, in: Brainscales General Meeting, Barcelona, March 2011.
46G. Hermann, J. Touboul, O. Faugeras.

Noise-induced behaviors in neural mean field equations, in: Brainscales General Meeting, Barcelona, March 2011.
47R. Veltz, O. Faugeras.

Interplay between constant delays and space dependent delays in neural fields models, in: Brainscales General Meeting, Barcelona, March 2011.

International Conferences with Proceedings

48W. Bel Haj Ali, E. Debreuve, P. Kornprobst, M. Barlaud.

Bio-Inspired Bags-of-Features for Image Classification, in: KDIR International Conference on Knowledge Discovery and Information Retrieval, October 2011.
49M.-J. Escobar, G. Masson, P. Kornprobst.

Can V1 surround suppression mechanism explain MT motion integration?, in: International Conference on Cognitive and Neural Systems (ICCNS), 2011.
50M.-J. Escobar, G. Masson, P. Kornprobst.

How MT neurons get influenced by V1 surround suppression?, in: Perception ECVP, September 2011.
51K. Masmoudi, M. Antonini, P. Kornprobst.

A Biologically Inspired Image Coder with Temporal Scalability, in: Advanced Concepts for Intelligent Vision Systems (ACIVS), 2011.

http://acivs.org/acivs2011/#bestpaper
52E. Tlapale, P. Kornprobst, G. Masson, O. Faugeras.

A Neural Field Model for Motion Estimation, in: Mathematical Image Processing, Springer Proceedings in Mathematics, Springer, 2011, vol. 5, p. 159–180.

http://dx.doi.org/10.1007/978-3-642-19604-1

Conferences without Proceedings

53J. Rankin, E. Tlapale, R. Veltz, P. Kornprobst, O. Faugeras.

Multistability and bifurcations in a model of motion perception, in: Developments in Dynamical Systems Arising from the Biosciences, March 2011.

http://www.mbi.ohio-state.edu/2010/ddsdescription.html

Scientific Books (or Scientific Book chapters)

54B. Cessac, A. Palacios.

7, in: "Spike train statistics from empirical facts to theory: the case of the retina" in Mathematical Problems in Computational Biology and Biomedicine, F. Cazals and P. Kornprobst editors., Springer, 2011, submitted.

Internal Reports

55J. Baladron, D. Fasoli, O. Faugeras, J. Touboul.

Mean Field description of and propagation of chaos in recurrent multipopulation networks of Hodgkin-Huxley and FitzHugh-Nagumo neurons, arXiv, 2011, Submitted to the Journal of Mathematical Neuroscience.

http://arxiv.org/abs/1110.4294
56K. Masmoudi, M. Antonini, P. Kornprobst.

Frames for Exact Inversion of the Rank Order Coder, INRIA Research Report, 2011, n^o RR-7744.

http://hal.inria.fr/inria-00627075/fr/
57J. Rankin, E. Tlapale, R. Veltz, O. Faugeras, P. Kornprobst.

Bifurcation analysis applied to a model of motion integration with a multistable stimulus, INRIA Research Report, 2011, n^o RR-7822.
58J.-C. Vasquez, T. Viéville, B. Cessac.

Parametric Estimation of Gibbs distributions as generalized maximum-entropy models for the analysis of spike train statistics., INRIA, 2011.
59J.-C. Vasquez, T. Viéville, B. Cessac.

Parametric Estimation of Gibbs distributions as generalized maximum-entropy models for the analysis of spike train statistics., INRIA, 2011.

http://hal.inria.fr/inria-00574954/PDF/RR-7561.pdf

Other Publications

60R. Cofre-Torres.

Statistics of spike trains in conductance-based neural networks: Including Gap-junctions, Master of Computational Biology, 2011.
61J. Touboul, G. Hermann, O. Faugeras.

Noise-induced behaviors in neural mean field dynamics, 2011, arXiv.

http://arxiv.org/abs/1104.5425

References in notes

62E. Adelson, J. Bergen.

Spatiotemporal energy models for the perception of motion, in: Journal of the Optical Society of America A, 1985, vol. 2, p. 284–299.
63J. Baladron, D. Fasoli, O. Faugeras.

Three applications of GPU computing in neuroscience, in: Computing in Science and Engineering, 2012.
64P. Chossat, O. Faugeras.

Hyperbolic planforms in relation to visual edges and textures perception, in: Plos Comput Biol, December 2009, vol. 5, n^o 12, e1000625 p.

http://dx.doi.org/doi:10.1371/journal.pcbi.1000625
65M.-J. Escobar.

Bio-Inspired Models for Motion Estimation and Analysis: Human action recognition and motion integration, Université de Nice Sophia-Antipolis, 2009.
66G. Hermann.

Some mean field equations in neuroscience, Ecole Polytechnique, January 2012.
67P. Kornprobst, E. Tlapale, J. Bouecke, H. Neumann, G. Masson.

A Bio-Inspired Evaluation Methodology for Motion Estimation, in: VSS, 2010.

http://www-sop.inria.fr/neuromathcomp/data/motionpsychobench/
68T. Masquelier.

Relative spike time coding and STDP-based orientation selectivity in the early visual system in natural continuous and saccadic vision: a computational model, in: Journal of Computational Neuroscience, 2011.

http://dx.doi.org/10.1007/s10827-011-0361-9
69E. Tlapale, P. Kornprobst, J. Bouecke, H. Neumann, G. Masson.

Evaluating motion estimation models from behavioural and psychophysical data, in: BIONETICS, 2010.
70E. Tlapale, P. Kornprobst, J. Bouecke, H. Neumann, G. Masson.

Towards a bio-inspired evaluation methodology for motion estimation models, INRIA, June 2010, n^o RR-7317.

http://hal.inria.fr/inria-00492001/PDF/RR-7317.pdf
71E. Tlapale, G. Masson, P. Kornprobst.

Modelling the dynamics of motion integration with a new luminance-gated diffusion mechanism, in: Vision Research, August 2010, vol. 50, n^o 17, p. 1676–1692.

http://dx.doi.org/10.1016/j.visres.2010.05.022
72J. Touboul, G. Hermann, O. Faugeras.

Noise-induced behaviors in neural mean field dynamics, in: SIAM Journal on Applied dynamical Systems, 2012.
73A. Wohrer, P. Kornprobst.

Virtual Retina : A biological retina model and simulator, with contrast gain control, in: Journal of Computational Neuroscience, 2009, vol. 26, n^o 2, 219 p, DOI 10.1007/s10827-008-0108-4.
74A. Wohrer.

Model and large-scale simulator of a biological retina with contrast gain control, University of Nice Sophia-Antipolis, 2008.

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