Bibliography

Major publications by the team in recent years

[1]

G. 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.

[2]

R. Brette, W. Gerstner.
Adaptive exponential integrate-and-fire model as an effective description of neuronal activity, in: Journal of Neurophysiology, 2005, vol. 94, p. 3637–3642.

[3]

B. 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.

[4]

M.-J. Escobar, G. S. Masson, T. Viéville, P. Kornprobst.
Action Recognition Using a Bio-Inspired Feedforward Spiking Network, in: International Journal of Computer Vision, 2009, vol. 82, n^o 3, 284 p
ftp://ftp-sop.inria.fr/neuromathcomp/publications/2009/escobar-masson-etal:09.pdf.

[5]

F. Grimbert, O. Faugeras.
Analysis of Jansen's model of a single cortical column, INRIA, jun 2005, n^o RR-5597, Technical report.

[6]

M. Samuelides, B. Cessac.
Random Recurrent Neural Networks, in: European Physical Journal - Special Topics, 2007, vol. 142, p. 7–88.

[7]

J. 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.

[8]

J. Touboul.
Importance of the cutoff value in the quadratic adaptive integrate-and-fire model, in: Neural Comput., 2009, vol. 21, n^o 8, p. 2114–2122
http://dx.doi.org/10.1162/neco.2009.09-08-853.

[9]

A. 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

[10]

M.-J. Escobar.
Bio-Inspired Models for Motion Estimation and Analysis: Human action recognition and motion integration, Université de Nice Sophia-Antipolis, 2009
ftp://ftp-sop.inria.fr/neuromathcomp/publications/phds/escobar:09.pdf, Ph. D. Thesis.

Articles in International Peer-Reviewed Journal

[11]

G. Aubert, P. Kornprobst.
Can the nonlocal characterization of Sobolev spaces by Bourgain etal. be useful to solve variational problems?, in: SIAM Journal on Numerical Analysis, feb 2009, vol. 47, n^o 2, p. 844–860
http://link.aip.org/link/?SNA/47/844.

[12]

B. Cessac.
A view of Neural Networks as dynamical systems, in: International Journal of Bifurcations and Chaos, 2009
http://lanl.arxiv.org/abs/0901.2203, to appear.

[13]

B. Cessac, H. Paugam-Moisy, T. Viéville.
Indisputable facts when implementing spiking neuron networks, in: J. Physiol., 2009
http://arxiv.org/abs/0903.3498, to appear.

[14]

B. Cessac, H. Rostro-Gonzalez, J. C. Vasquez, T. Viéville.
How Gibbs distribution may naturally arise from synaptic adaptation mechanisms: a model based argumentation, in: J. Stat. Phys,, 2009, vol. 136, n^o 3, p. 565-602
http://lanl.arxiv.org/abs/0812.3899.

[15]

P. Chossat, O. Faugeras.
Hyperbolic planforms in relation to visual edges and textures perception, in: Plos Computational Biology, 2009, Accepted for publication 11/04/2009..

[16]

T. Deneux, O. Faugeras.
EEG-fMRI Fusion of Paradigm-free Activity using Kalman Filtering, in: Neural Computation, 2009, Accepted for publication, 08/05/2009..

[17]

M.-J. Escobar, G. S. Masson, T. Viéville, P. Kornprobst.
Action Recognition Using a Bio-Inspired Feedforward Spiking Network, in: International Journal of Computer Vision, 2009, vol. 82, n^o 3, 284 p
ftp://ftp-sop.inria.fr/neuromathcomp/publications/2009/escobar-masson-etal:09.pdf.

[18]

O. 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.

[19]

O. Faugeras, R. Veltz, F. Grimbert.
Persistent neural states: stationary localized activity patterns in nonlinear continuous n-population, q-dimensional neural networks, in: Neural Computation, 2009, vol. 21, n^o 1, p. 147–187.

[20]

G. Faye, O. Faugeras.
Some theoretical and numerical results for delayed neural field equations, in: Physica D, 2009, Special issue on Mathematical Neuroscience..

[21]

S. Paris, P. Kornprobst, J. Tumblin, F. Durand.
Bilateral Filtering: Theory and Applications, in: Foundations and Trends in Computer Graphics and Vision, 2009, vol. 4, n^o 1
http://dx.doi.org/10.1561/0600000020.

[22]

H. Rostro-Gonzalez, B. Cessac, J. C. Vasquez, T. Viéville.
Back-engineering in spiking neural networks parameters, in: BMC Neuroscience, 2009, vol. 10, 289 p.

[23]

H. Rostro-Gonzalez, B. Cessac, J. C. Vasquez, T. Viéville.
Back-engineering of spiking neural networks parameters., in: Journal of Computational Neuroscience, 2009, to appear.

[24]

J. Touboul, R. Brette.
Spiking Dynamics of Bidimensional Integrate-and-Fire Neurons, in: SIAM Journal on Applied Dynamical Systems, 2009, vol. 8, p. 1462-1506
http://link.aip.org/link/?SJA/8/1462/1.

[25]

J. Touboul.
Importance of the cutoff value in the quadratic adaptive integrate-and-fire model, in: Neural Comput., 2009, vol. 21, n^o 8, p. 2114–2122
http://dx.doi.org/10.1162/neco.2009.09-08-853.

[26]

J. C. Vasquez, B. Cessac, H. Rostro-Gonzalez, T. Viéville.
How Gibbs Distributions may naturally arise from synaptic adaptation mechanisms, in: BMC Neuroscience, 2009, vol. 10, 213 p.

[27]

A. 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.

International Peer-Reviewed Conference/Proceedings

[28]

N. Bruce, P. Kornprobst.
Harris Corners in the Real World: A Principled Selection Criterion for Interest Points Based on Ecological Statistics, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition, 2009.

[29]

N. Bruce, P. Kornprobst.
On the role of context in probabilistic models of visual saliency, in: Proceedings of the International Conference on Image Processing, 2009.

[30]

T. Deneux, O. Faugeras.
EEG-fMRI Fusion of Paradigm-free Activity using Kalman Filtering, in: Proceedings of the Annual Meeting of the Society for Neuroscience, 2009.

[31]

H. Rostro-Gonzalez, B. Cessac, J. C. Vasquez, T. Viéville.
Back-engineering of spiking neural networks parameters, in: Computational Neurosciences meeting (CNS), 2009.

Workshops without Proceedings

[32]

B. Cessac, J. C. Vasquez, T. Viéville.
Parametric Estimation of spike train statistics, in: CNS 09 Berlin, 2009.

[33]

O. Faugeras, J. Touboul, B. Cessac.
A constructive mean-field analysis of multi population neural networks with random synaptic weights, in: COSYNE 09, 2009.

Internal Reports

[34]

P. Chossat, O. Faugeras.
Hyperbolic planforms in relation to visual edges and textures perception, arXiv, 2009
http://arxiv.org/pdf/0907.0963v3, Technical report.

[35]

E. Tlapale, G. S. Masson, P. Kornprobst.
A neural model of luminance-gated recurrent motion diffusion for 2D motion integration and segmentation, INRIA, 2009, n^o 6944
http://hal.inria.fr/inria-00360277/fr/, Technical report.

[36]

J. Touboul, O. Faugeras.
A Markovian event-based framework for stochastic spiking neural networks, arXiv, 2009
http://arxiv.org/pdf/0911.3462, Submitted to Neural Computation..

[37]

R. Veltz, O. Faugeras.
Local/global analysis of the stationary solutions of some neural field equations, arXiv, 2009
http://arxiv.org/pdf/0910.2247v1, Technical report.

[38]

A. Wohrer, P. Kornprobst, M. Antonini.
Retinal filtering and image reconstruction, INRIA, jun 2009, n^o 6960
http://hal.inria.fr/inria-00394547/fr/, Technical report.

Other Publications

[39]

H. Berry, B. Cessac.
Du chaos dans les neurones, nov 2009, Pour la Science.

References in notes

[40]

R. Brette, M. Rudolph, T. Carnevale, M. Hines, D. Beeman, J. M. Bower, M. Diesmann, A. Morrison, P. H. Goodman, F. C. Jr. Harris, M. Zirpe, T. Natschläger, D. Pecevski, B. Ermentrout, M. Djurfeldt, A. Lansner, O. Rochel, T. Viéville, E. Muller, A. P. Davison, S. E. Boustani, A. Destexhe.
Simulation of networks of spiking neurons: a review of tools and strategies, in: Journal of Computational Neuroscience, 2007, vol. 23, n^o 3, p. 349–398.

[41]

M.-J. Escobar, P. Kornprobst.
Action Recognition with a Bio–Inspired Feedforward Motion Processing Model: The Richness of Center-Surround Interactions, in: Proceedings of the 10th European Conference on Computer Vision, LNCS, Springer–Verlag, oct 2008, vol. 5305, p. 186–199
ftp://ftp-sop.inria.fr/odyssee/Publications/2008/escobar-kornprobst:08.pdf.

[42]

M.-J. Escobar, G. S. Masson, P. Kornprobst.
A Simple Mechanism to Reproduce the Neural Solution of the Aperture Problem in Monkey Area MT, in: Deuxième conférence française de Neurosciences Computationnelles, 2008
ftp://ftp-sop.inria.fr/odyssee/Publications/2008/escobar-masson-etal:08.pdf.

[43]

O. Faugeras, T. Luong, S. Maybank.
Camera self-calibration: theory and experiments, in: Proceedings of the 2nd European Conference on Computer Vision, Santa Margherita, Italy, G. Sandini (editor), Springer–Verlag, May 1992, p. 321–334.

[44]

B. H. Jansen, V. G. Rit.
Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns, in: Biological Cybernetics, 1995, vol. 73, p. 357–366.

[45]

H. Jhuang, T. Serre, L. Wolf, T. Poggio.
A biologically inspired system for action recognition, in: Proceedings of the 11th International Conference on Computer Vision, 2007, p. 1–8.

[46]

J. Reutimann, M. Giugliano, S. Fusi.
Event-driven simulation of spiking neurons with stochastic dynamics, in: Neural Computation, 2003, vol. 15, p. 811-830.

[47]

O. Shriki, D. Hansel, H. Sompolinsky.
Rate models for conductance-based cortical neuronal networks, in: Neural Computation, 2003, vol. 15, n^o 8, p. 1809–1841.

[48]

E. Tlapale, G. S. Masson, P. Kornprobst.
Motion Integration Modulated by Form Information, in: Deuxième conférence française de Neurosciences Computationnelles, 2008.

[49]

T. Turova, W. Mommaerts, E. Van Der Meulen.
Synchronization of firing times in a stochastic neural network model with excitatory connections, in: Stochastic processes and their applications, 1994, vol. 50, n^o 1, p. 173–186.

[50]

A. Wohrer.
Mathematical study of a neural gain control mechanism, INRIA, 2007, n^o 6327, Research report.

[51]

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

[52]

A. Wohrer.
The vertebrate retina: A functional review, INRIA, May 2008, n^o 6532, Research Report.