Bibliography

Major publications by the team in recent years

• 1J. 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, n^o 1.
  http://www.mathematical-neuroscience.com/content/2/1/10
• 2B. Cessac.
  A discrete time neural network model with spiking neurons II. Dynamics with noise, in: J. Math. Biol., 2011, vol. 62, pp. 863-900.
• 3P. 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.
  http://dx.doi.org/doi:10.1371/journal.pcbi.1000625
• 4R. 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
• 5R. Cofre, B. Cessac.
  Exact computation of the Maximum Entropy Potential of spiking neural networks models, in: Physical Reviev E, 2014, vol. 89, n^o 052117, 13 p.
  https://hal.inria.fr/hal-01095599
• 6O. 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, n^o 1, pp. 205–250.
• 7O. 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://arxiv.org/abs/0808.1113
• 8J. 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, n^o 38, pp. 15032-15043. [ DOI : 10.1523/JNEUROSCI.0870-13.2013 ]
  https://hal.inria.fr/hal-00844218
• 9E. 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, n^o 17, pp. 1676–1692.
  http://dx.doi.org/10.1016/j.visres.2010.05.022
• 10J. 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
• 11R. 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, n^o 3, pp. 954–998. [ DOI : 10.1137/090773611 ]
  http://arxiv.org/abs/0910.2247
• 12R. 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
• 13R. Veltz.
  Nonlinear analysis methods in neural field models, Université Paris Est, 2011.
  ftp://ftp-sop.inria.fr/neuromathcomp/publications/phds/veltz-11.pdf
• 14A. 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

• 15M. 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

• 16P. Beltrame, P. Chossat.
  Onset of intermittent octahedral patterns in spherical Bénard convection, in: European Journal of Mechanics - B/Fluids, April 2015, n^o 50, pp. 156-174. [ DOI : 10.1016/j.euromechflu.2014.11.014 ]
  https://hal.archives-ouvertes.fr/hal-01144925
• 17E. 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
• 18M. 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, n^o 1, 19 p. [ DOI : 10.1186/s13408-015-0031-8 ]
  https://hal.inria.fr/hal-01098582
• 19J. 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
• 20P. 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
• 21F. 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
• 22D. 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
• 23O. Faugeras, J. Maclaurin.
  Asymptotic description of neural networks with correlated synaptic weights, in: Entropy, July 2015, vol. 17(7), 4701-4743, n^o 7, pp. 4701-4743.
  https://hal.inria.fr/hal-00955770
• 24S. 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
• 25O. Podvigina, P. Chossat.
  Simple heteroclinic cycles in R${}^4$, in: Nonlinearity, 2015, vol. 28, n^o 4, pp. 901-926. [ DOI : 10.1088/0951-7715/28/4/901 ]
  https://hal.archives-ouvertes.fr/hal-01144934
• 26F. 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
• 27R. 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
• 28R. 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
• 29R. Veltz, T. J. Sejnowski.
  Periodic Forcing of Inhibition-Stabilized Networks: Nonlinear Resonances and Phase-Amplitude Coupling, in: Neural Computation, December 2015, vol. 27, n^o 12. [ DOI : 10.1162/NECO_a_00786 ]
  https://hal.inria.fr/hal-01096590

Invited Conferences

• 30B. 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
• 31B. Cessac.
  Mean Field Methods in Neuroscience, in: Dynamics of Multi-Level Systems, Dresde, Germany, June 2015.
  https://hal.inria.fr/hal-01235314
• 32B. Cessac.
  Mean Field TheoriesNeuroscience, in: QFT methods in stochastic nonlinear dynamics, Bielefeld, Germany, March 2015.
  https://hal.inria.fr/hal-01235311
• 33B. 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
• 34B. 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
• 35T. 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

• 36B. Cessac.
  Statistical analysis of spike trains in neuronal networks, in: Neuroscience and modellint, Paris, France, December 2015.
  https://hal.inria.fr/hal-01246092
• 37M. 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
• 38R. 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
• 39C. 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

• 40N. 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, n^o RR-8695, 19 p.
  https://hal.inria.fr/hal-01131099
• 41N. 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, n^o RR-8696, Published in the 23rd European Signal Processing Conference (EUSIPCO).
  https://hal.inria.fr/hal-01131100
• 42N. 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, n^o RR-8698, 57 p.
  https://hal.inria.fr/hal-01131645

Other Publications

• 43D. Avitabile, M. Desroches, E. Knobloch, M. Krupa.
  Ducks in space, November 2015, submitted for publication.
  https://hal.inria.fr/hal-01243304
• 44P. 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
• 45M. 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
• 46M. 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
• 47M. 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
• 48G. 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
• 49J. 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
• 50J. 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
• 51T. 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
• 52D. 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
• 53D. 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
• 54C. 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
• 55R. 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

• 56G. 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.
• 57J. 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
• 58B. 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.
• 59B. Cessac.
  Statistics of spike trains in conductance-based neural networks: Rigorous results, in: The Journal of Mathematical Neuroscience, 2011, vol. 1, n^o 8, pp. 1-42. [ DOI : 10.1186/2190-8567-1-8 ]
  http://www.mathematical-neuroscience.com/content/1/1/8
• 60B. Cessac, R. Cofre.
  Spike train statistics and Gibbs distributions, in: Journal of Physiology - Paris, 2013, vol. 107, n^o 5, pp. 360-368.
  https://hal.inria.fr/hal-00850155
• 61B. 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, pp. 565-602. [ DOI : 10.1007/s10955-009-9786-1 ]
  http://lanl.arxiv.org/abs/0812.3899
• 62B. Cessac, T. Viéville.
  On Dynamics of Integrate-and-Fire Neural Networks with Adaptive Conductances, in: Frontiers in neuroscience, July 2008, vol. 2, n^o 2.
  https://hal.inria.fr/inria-00338369
• 63E. J. Chichilnisky.
  A simple white noise analysis of neuronal light responses, in: Network: Comput. Neural Syst., 2001, vol. 12, pp. 199–213.
• 64M. 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, n^o 18, pp. 7152–7157.
• 65M. 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, n^o 2, pp. 211–288.
• 66M. 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, n^o 4, 046106. [ DOI : 10.1063/1.4827026 ]
  https://hal.inria.fr/hal-00932344
• 67M. 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, n^o 4, pp. 1131–1162.
• 68M.-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, n^o 5, 593—605 p.
  http://hal.inria.fr/hal-00849935
• 69M.-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, pp. 284-301.
  ftp://ftp-sop.inria.fr/neuromathcomp/publications/2009/escobar-masson-etal:09.pdf
• 70P. Foldiak.
  Stimulus optimization in primary visual cortex, in: Neurocomputing, 2001, vol. 38, pp. 1217–1222.
• 71M. Galtier, O. Faugeras, P. Bressloff.
  Hebbian Learning of Recurrent Connections: A Geometrical Perspective, in: Neural Computation, September 2012, vol. 24, n^o 9, pp. 2346-2383.
• 72M. Galtier, G. Wainrib.
  Multiscale analysis of slow-fast neuronal learning models with noise, in: Journal of Mathematical Neuroscience, 2012, vol. 2, n^o 13.
  http://www.mathematical-neuroscience.com/content/2/1/13/abstract
• 73E. M. Izhikevich.
  Neural excitability, spiking and bursting, in: International Journal of Bifurcation and Chaos, 2000, vol. 10, n^o 06, pp. 1171–1266.
• 74B. 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.
• 75M. 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, n^o 1, 015106 p.
• 76M. Krupa, P. Szmolyan.
  Relaxation oscillation and canard explosion, in: Journal of Differential Equations, 2001, vol. 174, n^o 2, pp. 312–368.
• 77D. MacKay.
  Information-based objective functions for active data selection, in: Neural computation, 1992, vol. 4, n^o 4, pp. 590–604.
• 78C. K. Machens.
  Adaptive sampling by information maximization, in: Physical Review Letters, 2002, vol. 88, n^o 22.
• 79K. 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
• 80K. 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, n^o 2, pp. 353–359.
  http://dx.doi.org/10.1109/TNNLS.2011.2179557
• 81K. 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
• 82T. 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, n^o 3, pp. 425–441.
  http://dx.doi.org/10.1007/s10827-011-0361-9
• 83A. Mohemmed, G. Lu, N. Kasabov.
  Evaluating SPAN Incremental Learning for Handwritten Digit Recognition, in: Neural Information Processing, Berlin, Heidelberg, Springer, 2012, pp. 670–677.
• 84K. 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
• 85J. 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, n^o 1, pp. 103-124. [ DOI : 10.1007/s10827-012-0409-5 ]
  https://hal.inria.fr/hal-00845593
• 86N. 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.
• 87E. Simoncelli, D. Heeger.
  A Model of Neuronal Responses in Visual Area MT, in: Vision Research, 1998, vol. 38, pp. 743–761.
• 88B. 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.
• 89B. 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, n^o 12, 12 p.
• 90E. 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
• 91E. 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
• 92J. Touboul, F. Wendling, P. Chauvel, O. Faugeras.
  Neural Mass Activity, Bifurcations, and Epilepsy, in: Neural Computation, December 2011, vol. 23, n^o 12, pp. 3232–3286.
• 93P. 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
• 94R. Veltz, O. Faugeras.
  A Center Manifold Result for Delayed Neural Fields Equations, in: SIAM Journal on Mathematical Analysis, 2013, vol. 45, n^o 3, pp. 1527-1562. [ DOI : 10.1137/110856162 ]
  https://hal.inria.fr/hal-00850382
• 95R. Veltz, O. Faugeras.
  A Center Manifold Result for Delayed Neural Fields Equations, in: SIAM Journal on Mathematical Analysis, 2013, vol. 45, n^o 3, pp. 1527-562.