Members
Overall Objectives
Research Program
Highlights of the Year
New Software and Platforms
New Results
Partnerships and Cooperations
Dissemination
Bibliography
XML PDF e-pub
PDF e-Pub


Bibliography

Major publications by the team in recent years
[1]
J. Baladron, D. Fasoli, O. Faugeras, J. Touboul.
Mean-field description and propagation of chaos in networks of Hodgkin-Huxley neurons, in: The Journal of Mathematical Neuroscience, 2012, vol. 2, no 1.
http://www.mathematical-neuroscience.com/content/2/1/10
[2]
B. Cessac.
A discrete time neural network model with spiking neurons II. Dynamics with noise, in: J. Math. Biol., 2011, vol. 62, pp. 863-900.
[3]
P. Chossat, O. Faugeras.
Hyperbolic planforms in relation to visual edges and textures perception, in: Plos Comput Biol, December 2009, vol. 5, no 12, e1000625.
http://dx.doi.org/doi:10.1371/journal.pcbi.1000625
[4]
R. Cofre, B. Cessac.
Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses, in: Chaos, Solitons and Fractals, 2012, submitted.
http://lanl.arxiv.org/abs/1212.3577
[5]
R. Cofre, B. Cessac.
Exact computation of the Maximum Entropy Potential of spiking neural networks models, in: Physical Reviev E, 2014, vol. 89, no 052117, 13 p.
https://hal.inria.fr/hal-01095599
[6]
O. Faugeras, F. Grimbert, J.-J. Slotine.
Abolute stability and complete synchronization in a class of neural fields models, in: SIAM journal of applied mathematics, September 2008, vol. 61, no 1, pp. 205–250.
[7]
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, no 1. [ DOI : 10.3389/neuro.10.001.2010 ]
http://arxiv.org/abs/0808.1113
[8]
J. Naudé, B. Cessac, H. Berry, B. Delord.
Effects of Cellular Homeostatic Intrinsic Plasticity on Dynamical and Computational Properties of Biological Recurrent Neural Networks, in: Journal of Neuroscience, 2013, vol. 33, no 38, pp. 15032-15043. [ DOI : 10.1523/JNEUROSCI.0870-13.2013 ]
https://hal.inria.fr/hal-00844218
[9]
E. Tlapale, G. S. Masson, P. Kornprobst.
Modelling the dynamics of motion integration with a new luminance-gated diffusion mechanism, in: Vision Research, August 2010, vol. 50, no 17, pp. 1676–1692.
http://dx.doi.org/10.1016/j.visres.2010.05.022
[10]
J. 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
[11]
R. Veltz, O. Faugeras.
Local/Global Analysis of the Stationary Solutions of Some Neural Field Equations, in: SIAM Journal on Applied Dynamical Systems, August 2010, vol. 9, no 3, pp. 954–998. [ DOI : 10.1137/090773611 ]
http://arxiv.org/abs/0910.2247
[12]
R. Veltz, O. Faugeras.
A center manifold result for delayed neural fields equations, in: SIAM Journal on Applied Mathematics (under revision), July 2012, RR-8020.
http://hal.inria.fr/hal-00719794
[13]
R. Veltz.
Nonlinear analysis methods in neural field models, Université Paris Est, 2011.
ftp://ftp-sop.inria.fr/neuromathcomp/publications/phds/veltz-11.pdf
[14]
A. Wohrer, P. Kornprobst.
Virtual Retina : A biological retina model and simulator, with contrast gain control, in: Journal of Computational Neuroscience, 2009, vol. 26, no 2, 219 p, DOI 10.1007/s10827-008-0108-4.
Publications of the year

Doctoral Dissertations and Habilitation Theses

[15]
M. Desroches.
Complex oscillations with multiple timescales - Application to neuronal dynamics , Universite Pierre et Marie Curie, December 2015, Habilitation à diriger des recherches.
https://hal.inria.fr/tel-01254956

Articles in International Peer-Reviewed Journals

[16]
P. Beltrame, P. Chossat.
Onset of intermittent octahedral patterns in spherical Bénard convection, in: European Journal of Mechanics - B/Fluids, April 2015, no 50, pp. 156-174. [ DOI : 10.1016/j.euromechflu.2014.11.014 ]
https://hal.archives-ouvertes.fr/hal-01144925
[17]
E. Benoît, M. Brøns, M. Desroches, M. Krupa.
Extending the zero-derivative principle for slow–fast dynamical systems, in: Zeitschrift für Angewandte Mathematik und Physik (ZAMP), July 2015. [ DOI : 10.1007/s00033-015-0552-8 ]
https://hal.inria.fr/hal-01243307
[18]
M. Bossy, O. Faugeras, D. Talay.
Clarification and Complement to " Mean-Field Description and Propagation of Chaos in Networks of Hodgkin–Huxley and FitzHugh–Nagumo Neurons ", in: Journal of Mathematical Neuroscience, 2015, vol. 5, no 1, 19 p. [ DOI : 10.1186/s13408-015-0031-8 ]
https://hal.inria.fr/hal-01098582
[19]
J. Burke, M. Desroches, A. Granados, T. J. Kaper, M. Krupa, T. Vo.
From Canards of Folded Singularities to Torus Canards in a Forced van der Pol Equation, in: Journal of Nonlinear Science, November 2015. [ DOI : 10.1007%2Fs00332-015-9279-0 ]
https://hal.inria.fr/hal-01242892
[20]
P. Chossat, M. Krupa.
Heteroclinic cycles in Hopfield networks, in: Journal of Nonlinear Science, January 2016. [ DOI : 10.1007/s00332-015-9276-3 ]
https://hal.inria.fr/hal-01096505
[21]
F. Delarue, J. Inglis, S. Rubenthaler, E. Tanré.
Particle systems with a singular mean-field self-excitation. Application to neuronal networks, in: Stochastic Processes and Applications, 2015, vol. 125, pp. 2451–2492. [ DOI : 10.1016/j.spa.2015.01.007 ]
https://hal.inria.fr/hal-01001716
[22]
D. Fasoli, O. Faugeras, S. Panzeri.
A Formalism for Evaluating Analytically the Cross-Correlation Structure of a Firing-Rate Network Model, in: journal of mathematical neurosciences, March 2015. [ DOI : 10.1186/s13408-015-0020-y ]
https://hal.inria.fr/hal-01208576
[23]
O. Faugeras, J. Maclaurin.
Asymptotic description of neural networks with correlated synaptic weights, in: Entropy, July 2015, vol. 17(7), 4701-4743, no 7, pp. 4701-4743.
https://hal.inria.fr/hal-00955770
[24]
S. Fernández-García, M. Desroches, M. Krupa, A. Teruel.
Canard solutions in planar piecewise linear systems with three zones, in: Dynamical Systems, September 2015. [ DOI : 10.1080/14689367.2015.1079304 ]
https://hal.inria.fr/hal-01244978
[25]
O. Podvigina, P. Chossat.
Simple heteroclinic cycles in R4, in: Nonlinearity, 2015, vol. 28, no 4, pp. 901-926. [ DOI : 10.1088/0951-7715/28/4/901 ]
https://hal.archives-ouvertes.fr/hal-01144934
[26]
F. Solari, M. Chessa, N. V. K. Medathati, P. Kornprobst.
What can we expect from a V1-MT feedforward architecture for optical flow estimation?, in: Signal Processing: Image Communication, 2015. [ DOI : 10.1016/j.image.2015.04.006 ]
https://hal.inria.fr/hal-01215519
[27]
R. Veltz, P. Chossat, O. Faugeras.
On the effects on cortical spontaneous activity of the symmetries of the network of pinwheels in visual area V1, in: Journal of Mathematical Neuroscience, May 2015. [ DOI : 10.1186/s13408-015-0023-8 ]
https://hal.inria.fr/hal-01079055
[28]
R. Veltz, O. Faugeras.
ERRATUM: A Center Manifold Result for Delayed Neural Fields Equations, in: SIAM Journal on Mathematical Analysis, 2015. [ DOI : 10.1137/140962279 ]
https://hal.inria.fr/hal-01096598
[29]
R. Veltz, T. J. Sejnowski.
Periodic Forcing of Inhibition-Stabilized Networks: Nonlinear Resonances and Phase-Amplitude Coupling, in: Neural Computation, December 2015, vol. 27, no 12. [ DOI : 10.1162/NECO_a_00786 ]
https://hal.inria.fr/hal-01096590

Invited Conferences

[30]
B. Cessac.
Confronting mean-field theories to measurements a perspective from neuroscience, in: Confronting mean-field theories to measurements a perspective from neuroscience, Paris, France, January 2015.
https://hal.inria.fr/hal-01225619
[31]
B. Cessac.
Mean Field Methods in Neuroscience, in: Dynamics of Multi-Level Systems, Dresde, Germany, June 2015.
https://hal.inria.fr/hal-01235314
[32]
B. Cessac.
Mean Field TheoriesNeuroscience, in: QFT methods in stochastic nonlinear dynamics, Bielefeld, Germany, March 2015.
https://hal.inria.fr/hal-01235311
[33]
B. Cessac.
Statistical models for spike trains analysis in the retina, in: 12eme Colloque de la société des neurosciences, Montpellier, France, May 2015.
https://hal.inria.fr/hal-01235313
[34]
B. Cessac, J. Naudé, H. Berry, B. Delord.
Control of recurrent neural network dynamics byhomeostatic intrinsic plasticity, in: Workshop on neural population dynamics, Gif sur Yvette, France, February 2015.
https://hal.inria.fr/hal-01225622
[35]
T. Karvouniari, L. Gil, B. Cessac.
Biophysical reaction-diffusion model for stage II retinal waves and bifurcations analysis, in: MathStatNeuro workshop, Nice, France, September 2015.
https://hal.inria.fr/hal-01211548

Conferences without Proceedings

[36]
B. Cessac.
Statistical analysis of spike trains in neuronal networks, in: Neuroscience and modellint, Paris, France, December 2015.
https://hal.inria.fr/hal-01246092
[37]
M. Chessa, N. V. K. Medathati, G. S. Masson, F. Solari, P. Kornprobst.
Decoding MT Motion Response For Optical Flow Estimation : An Experimental Evaluation, in: 23rd European Signal Processing Conference (EUSIPCO), Nice, France, August 2015.
https://hal.inria.fr/hal-01215526
[38]
R. Cofre, B. Cessac.
Spatio-Temporal Linear Response of Spiking Neuronal Network Models, in: ISCLANE 15, Barcelone, Spain, September 2015.
https://hal.inria.fr/hal-01235318
[39]
C. Hilario Gomez, N. V. K. Medathati, P. Kornprobst, V. Murino, D. Sona.
Improving FREAK Descriptor for Image Classification, in: The 10th International Conference on Computer Vision Systems (ICVS 2015), Nice, France, July 2015.
https://hal.inria.fr/hal-01205376

Internal Reports

[40]
N. V. K. Medathati, M. Chessa, G. S. Masson, P. Kornprobst, F. Solari.
Adaptive Motion Pooling and Diffusion for Optical Flow, Inria Sophia-Antipolis ; University of Genoa ; INT la Timone, March 2015, no RR-8695, 19 p.
https://hal.inria.fr/hal-01131099
[41]
N. V. K. Medathati, M. Chessa, G. S. Masson, P. Kornprobst, F. Solari.
Decoding MT Motion Response for Optical Flow Estimation: An Experimental Evaluation, Inria Sophia-Antipolis, France ; University of Genoa, Genoa, Italy ; INT la Timone, Marseille, France ; Inria, March 2015, no RR-8696, Published in the 23rd European Signal Processing Conference (EUSIPCO).
https://hal.inria.fr/hal-01131100
[42]
N. V. K. Medathati, H. Neumann, G. S. Masson, P. Kornprobst.
Bio-Inspired Computer Vision: Setting the Basis for a New Departure, Inria Sophia Antipolis, France ; Institut de Neurosciences de la Timone, Marseille, France ; University of Ulm, Germany ; Inria, March 2015, no RR-8698, 57 p.
https://hal.inria.fr/hal-01131645

Other Publications

[43]
D. Avitabile, M. Desroches, E. Knobloch, M. Krupa.
Ducks in space, November 2015, submitted for publication.
https://hal.inria.fr/hal-01243304
[44]
P. Bressloff, O. Faugeras.
On the Hamiltonian structure of large deviations in stochastic hybrid systems, September 2015, working paper or preprint.
https://hal.inria.fr/hal-01072077
[45]
M. Desroches, S. Fernández-García, M. Krupa.
Canards and spike-adding transitions in a minimal piecewise-linear Hindmarsh-Rose square-wave burster, December 2015, submitted for publication.
https://hal.inria.fr/hal-01243302
[46]
M. Desroches, A. Guillamon, E. Ponce, R. Prohens, S. Rodrigues, A. Teruel.
Canards, folded nodes and mixed-mode oscillations in piecewise-linear slow-fast systems, March 2015, accepted for publication in SIAM Review on 13 August 2015.
https://hal.inria.fr/hal-01243289
[47]
M. Desroches, M. Krupa, S. Rodrigues.
Spike-adding mechanism in parabolic bursters: the role of folded-saddle canards, December 2015, submitted for publication.
https://hal.inria.fr/hal-01136874
[48]
G. Hilgen, S. Softley, D. Pamplona, P. Kornprobst, B. Cessac, E. Sernagor.
The effect of retinal GABA Depletion by Allylglycineon mouse retinal ganglion cell responses to light, October 2015, European Retina Meeting, Poster.
https://hal.inria.fr/hal-01235324
[49]
J. Inglis, J. Maclaurin.
A general framework for stochastic traveling waves and patterns, with application to neural field equations, June 2015, 43 pages, 3 figures.
https://hal.archives-ouvertes.fr/hal-01169697
[50]
J. Inglis, D. Talay.
Mean-field limit of a stochastic particle system smoothly interacting through threshold hitting-times and applications to neural networks with dendritic component, September 2015, working paper or preprint.
https://hal.inria.fr/hal-01069398
[51]
T. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac.
Biophysical modelling of the intrinsic mechanisms of the autonomous starbust cells during stage II retinal waves, January 2016, Modelling the early visual system - Workshop, Poster.
https://hal.inria.fr/hal-01256477
[52]
D. Pamplona, B. Cessac, P. Kornprobst.
Shifting stimulus for faster receptive fields estimation of ensembles of neurons, March 2015, Computational and Systems Neuroscience (Cosyne), Poster.
https://hal.inria.fr/hal-01215537
[53]
D. Pamplona, G. Hilgen, S. Pirmoradian, M. H. Hennig, B. Cessac, E. Sernagor, P. Kornprobst.
A super-resolution approach for receptive fields estimation of neuronal ensembles, July 2015, Computational Neuroscience (CNS), Poster.
https://hal.inria.fr/hal-01215541
[54]
C. Ravello, R. Herzog, B. Cessac, M.-J. Escobar, A. Palacios.
Spectral dimension reduction on parametric models for spike train statistics, May 2015, 12e Colloque de la Société des Neurosciences , Poster.
https://hal.inria.fr/hal-01246088
[55]
R. Veltz.
A new twist for the simulation of hybrid systems using the true jump method, December 2015, working paper or preprint.
https://hal.inria.fr/hal-01243615
References in notes
[56]
G. Basalyga, M. A. Montemurro, T. Wennekers.
Information coding in a laminar computational model of cat primary visual cortex, in: J. Comput. Neurosci., 2013, vol. 34, pp. 273–83.
[57]
J. Bouecke, E. Tlapale, P. Kornprobst, H. Neumann.
Neural Mechanisms of Motion Detection, Integration, and Segregation: From Biology to Artificial Image Processing Systems, in: EURASIP Journal on Advances in Signal Processing, 2011, vol. 2011, special issue on Biologically inspired signal processing: Analysis, algorithms, and applications. [ DOI : 10.1155/2011/781561 ]
http://asp.eurasipjournals.com/content/2011/1/781561
[58]
B. Cessac.
A discrete time neural network model with spiking neurons. Rigorous results on the spontaneous dynamics, in: J. Math. Biol., 2008, vol. 56, pp. 311-345.
[59]
B. Cessac.
Statistics of spike trains in conductance-based neural networks: Rigorous results, in: The Journal of Mathematical Neuroscience, 2011, vol. 1, no 8, pp. 1-42. [ DOI : 10.1186/2190-8567-1-8 ]
http://www.mathematical-neuroscience.com/content/1/1/8
[60]
B. Cessac, R. Cofre.
Spike train statistics and Gibbs distributions, in: Journal of Physiology - Paris, 2013, vol. 107, no 5, pp. 360-368.
https://hal.inria.fr/hal-00850155
[61]
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, no 3, pp. 565-602. [ DOI : 10.1007/s10955-009-9786-1 ]
http://lanl.arxiv.org/abs/0812.3899
[62]
B. Cessac, T. Viéville.
On Dynamics of Integrate-and-Fire Neural Networks with Adaptive Conductances, in: Frontiers in neuroscience, July 2008, vol. 2, no 2.
https://hal.inria.fr/inria-00338369
[63]
E. J. Chichilnisky.
A simple white noise analysis of neuronal light responses, in: Network: Comput. Neural Syst., 2001, vol. 12, pp. 199–213.
[64]
M. O. Cunningham, M. A. Whittington, A. Bibbig, A. Roopun, F. E. LeBeau, A. Vogt, H. Monyer, E. H. Buhl, R. D. Traub.
A role for fast rhythmic bursting neurons in cortical gamma oscillations in vitro, in: Proceedings of the National Academy of Sciences of the United States of America, 2004, vol. 101, no 18, pp. 7152–7157.
[65]
M. Desroches, J. Guckenheimer, B. Krauskopf, C. Kuehn, H. M. Osinga, M. Wechselberger.
Mixed-mode oscillations with multiple time scales, in: SIAM Review, 2012, vol. 54, no 2, pp. 211–288.
[66]
M. Desroches, T. J. Kaper, M. Krupa.
Mixed-Mode Bursting Oscillations: Dynamics created by a slow passage through spike-adding canard explosion in a square-wave burster, in: Chaos, October 2013, vol. 23, no 4, 046106. [ DOI : 10.1063/1.4827026 ]
https://hal.inria.fr/hal-00932344
[67]
M. Desroches, B. Krauskopf, H. M. Osinga.
The geometry of slow manifolds near a folded node, in: SIAM Journal on Applied Dynamical Systems, 2008, vol. 7, no 4, pp. 1131–1162.
[68]
M.-J. Escobar, P. Kornprobst.
Action recognition via bio-inspired features: The richness of center-surround interaction, in: Computer Vision and Image Understanding, 2012, vol. 116, no 5, 593—605 p.
http://hal.inria.fr/hal-00849935
[69]
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, no 3, pp. 284-301.
ftp://ftp-sop.inria.fr/neuromathcomp/publications/2009/escobar-masson-etal:09.pdf
[70]
P. Foldiak.
Stimulus optimization in primary visual cortex, in: Neurocomputing, 2001, vol. 38, pp. 1217–1222.
[71]
M. Galtier, O. Faugeras, P. Bressloff.
Hebbian Learning of Recurrent Connections: A Geometrical Perspective, in: Neural Computation, September 2012, vol. 24, no 9, pp. 2346-2383.
[72]
M. Galtier, G. Wainrib.
Multiscale analysis of slow-fast neuronal learning models with noise, in: Journal of Mathematical Neuroscience, 2012, vol. 2, no 13.
http://www.mathematical-neuroscience.com/content/2/1/13/abstract
[73]
E. M. Izhikevich.
Neural excitability, spiking and bursting, in: International Journal of Bifurcation and Chaos, 2000, vol. 10, no 06, pp. 1171–1266.
[74]
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, pp. 357–366.
[75]
M. Krupa, N. Popović, N. Kopel, H. G. Rotstein.
Mixed-mode oscillations in a three time-scale model for the dopaminergic neuron, in: Chaos: An Interdisciplinary Journal of Nonlinear Science, 2008, vol. 18, no 1, 015106 p.
[76]
M. Krupa, P. Szmolyan.
Relaxation oscillation and canard explosion, in: Journal of Differential Equations, 2001, vol. 174, no 2, pp. 312–368.
[77]
D. MacKay.
Information-based objective functions for active data selection, in: Neural computation, 1992, vol. 4, no 4, pp. 590–604.
[78]
C. K. Machens.
Adaptive sampling by information maximization, in: Physical Review Letters, 2002, vol. 88, no 22.
[79]
K. Masmoudi, M. Antonini, P. Kornprobst.
Another look at the retina as an image scalar quantizer, in: Proceedings of the International Symposium on Circuits and Systems (ISCAS), 2010.
ftp://ftp-sop.inria.fr/neuromathcomp/publications/2010/masmoudi-antonini-etal:10c.pdf
[80]
K. Masmoudi, M. Antonini, P. Kornprobst.
Frames for Exact Inversion of the Rank Order Coder, in: IEEE Transactions on Neural Networks and Learning Systems, 2012, vol. 23, no 2, pp. 353–359.
http://dx.doi.org/10.1109/TNNLS.2011.2179557
[81]
K. Masmoudi, M. Antonini, P. Kornprobst.
Streaming an image through the eye: The retina seen as a dithered scalable image coder, in: Signal Processing-Image Communication, 2012.
http://dx.doi.org/10.1016/j.image.2012.07.005
[82]
T. 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, 2012, vol. 32, no 3, pp. 425–441.
http://dx.doi.org/10.1007/s10827-011-0361-9
[83]
A. Mohemmed, G. Lu, N. Kasabov.
Evaluating SPAN Incremental Learning for Handwritten Digit Recognition, in: Neural Information Processing, Berlin, Heidelberg, Springer, 2012, pp. 670–677.
[84]
K. Nasrollahi, T. B. Moeslund.
Super-resolution: a comprehensive survey, in: Machine Vision and Applications, 2014, vol. 25, pp. 1423–1468.
http://doi.org/10.1007/s00138-014-0623-4
[85]
J. Rankin, E. Tlapale, R. Veltz, O. Faugeras, P. Kornprobst.
Bifurcation analysis applied to a model of motion integration with a multistable stimulus, in: Journal of Computational Neuroscience, 2013, vol. 34, no 1, pp. 103-124. [ DOI : 10.1007/s10827-012-0409-5 ]
https://hal.inria.fr/hal-00845593
[86]
N. Rust, V. Mante, E. Simoncelli, J. Movshon.
How MT cells analyze the motion of visual patterns, in: Nature Neuroscience, 2006, vol. 9, pp. 1421–1431.
[87]
E. Simoncelli, D. Heeger.
A Model of Neuronal Responses in Visual Area MT, in: Vision Research, 1998, vol. 38, pp. 743–761.
[88]
B. Siri, H. Berry, B. Cessac, B. Delord, M. Quoy.
Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons, in: Journal of Physiology-Paris, 2007.
[89]
B. Siri, H. Berry, B. Cessac, B. Delord, M. Quoy.
A Mathematical Analysis of the Effects of Hebbian Learning Rules on the Dynamics and Structure of Discrete-Time Random Recurrent Neural Networks, in: Neural Computation, December 2008, vol. 20, no 12, 12 p.
[90]
E. Tlapale, P. Kornprobst, G. S. Masson, O. Faugeras.
A Neural Field Model for Motion Estimation, in: Mathematical Image Processing, S. Verlag (editor), Springer Proceedings in Mathematics, 2011, vol. 5, pp. 159–180.
http://dx.doi.org/10.1007/978-3-642-19604-1
[91]
E. Tlapale.
Modelling the dynamics of contextual motion integration in the primate, Université Nice Sophia Antipolis, January 2011.
ftp://ftp-sop.inria.fr/neuromathcomp/publications/phds/tlapale-11.pdf
[92]
J. Touboul, F. Wendling, P. Chauvel, O. Faugeras.
Neural Mass Activity, Bifurcations, and Epilepsy, in: Neural Computation, December 2011, vol. 23, no 12, pp. 3232–3286.
[93]
P. Vance, S. A. Coleman, D. Kerr, G. Das, T. McGinnity.
Modelling of a retinal ganglion cell with simple spiking models, in: IEEE Int. Jt. Conf. Neural Networks, 2015, pp. 1–8.
http://doi.org/10.1109/IJCNN.2015.7280759
[94]
R. Veltz, O. Faugeras.
A Center Manifold Result for Delayed Neural Fields Equations, in: SIAM Journal on Mathematical Analysis, 2013, vol. 45, no 3, pp. 1527-1562. [ DOI : 10.1137/110856162 ]
https://hal.inria.fr/hal-00850382
[95]
R. Veltz, O. Faugeras.
A Center Manifold Result for Delayed Neural Fields Equations, in: SIAM Journal on Mathematical Analysis, 2013, vol. 45, no 3, pp. 1527-562.