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Bibliography

Major publications by the team in recent years
  • 1R. Cofré, B. Cessac.

    Dynamics and spike trains statistics in conductance-based integrate-and-fire neural networks with chemical and electric synapses, in: Chaos, Solitons & Fractals, 2013, vol. 50, no 13, 3 p.
  • 2R. Cofré, B. Cessac.

    Exact computation of the maximum-entropy potential of spiking neural-network models, in: Phys. Rev. E, 2014, vol. 89, no 052117.
  • 3M.-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, 284 p.

    ftp://ftp-sop.inria.fr/neuromathcomp/publications/2009/escobar-masson-etal:09.pdf
  • 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, no 1. [ DOI : 10.3389/neuro.10.001.2010 ]

    http://arxiv.org/abs/0808.1113
  • 5D. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac.

    A biophysical model explains the oscillatory behaviour of immature starburst amacrine cells, 2018, submitted to Scientific Reports.

    https://hal.inria.fr/hal-01484133
  • 6T. Masquelier, G. Portelli, P. Kornprobst.

    Microsaccades enable efficient synchrony-based coding in the retina: a simulation study, in: Scientific Reports, April 2016, vol. 6, 24086. [ DOI : 10.1038/srep24086 ]

    http://hal.upmc.fr/hal-01301838
  • 7N. V. K. Medathati, H. Neumann, G. S. Masson, P. Kornprobst.

    Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision, in: Computer Vision and Image Understanding (CVIU), April 2016. [ DOI : 10.1016/j.cviu.2016.04.009 ]

    https://hal.inria.fr/hal-01316103
  • 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, no 38, pp. 15032-15043. [ DOI : 10.1523/JNEUROSCI.0870-13.2013 ]

    https://hal.inria.fr/hal-00844218
  • 9J. Rankin, A. I. Meso, G. S. Masson, O. Faugeras, P. Kornprobst.

    Bifurcation Study of a Neural Fields Competition Model with an Application to Perceptual Switching in Motion Integration, in: Journal of Computational Neuroscience, 2014, vol. 36, no 2, pp. 193–213.

    http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s10827-013-0465-5
  • 10A. 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

Articles in International Peer-Reviewed Journals

Invited Conferences

  • 13B. Cessac, D. Karvouniari, L. Gil, O. Marre, S. Picaud.

    Ion channels and properties of large neuronal networks: a computational study of re.nal waves during development, in: Symposium on Ion channels and Channelopathies - IPMC, Sophia Antipolis, France, November 2018.

    https://hal.archives-ouvertes.fr/hal-01925829
  • 14B. Cessac, D. Karvouniari, L. Gil, O. Marre, S. Picaud.

    Multi scale dynamics in retinal waves, in: Inspire New Insights on Complex Neural Dynamics, Cergy-Pontoise, France, June 2018.

    https://hal.inria.fr/hal-01816919

International Conferences with Proceedings

  • 15B. Cessac, R. Cofré.

    Linear Response of General Observables in Spiking Neuronal Network Models, in: ICMNS 2018 - 4th International Conférence on Mathematical Neuroscience, Juan les Pins, France, June 2018, pp. 1-70.

    https://hal.inria.fr/hal-01816920

Scientific Books (or Scientific Book chapters)

Internal Reports

  • 17J. Gautier, N. Chleq, P. Kornprobst.

    A Binocular LVA Device based on Mixed Reality to Enhance Face Recognition, Université Côte d’Azur, Inria, France, October 2018, no RR-9216, pp. 1-19.

    https://hal.inria.fr/hal-01900574

Other Publications

  • 18T. Andréoletti, B. Cessac, F. Chavane.

    Decoding cortical activity evoked by artificial retinal implants, ENSEA-Inria, August 2018.

    https://hal.inria.fr/hal-01895100
  • 19B. Cessac, R. Cofré.

    Linear response for spiking neuronal networks with unbounded memory, October 2018, https://arxiv.org/abs/1704.05344 - working paper or preprint.

    https://hal.inria.fr/hal-01895095
  • 20B. Cessac, D. Karvouniari, L. Gil, O. Marre, S. Picaud.

    Retinal waves: experiments and theory, June 2018, Journées scientifiques de l'Inria.

    https://hal.inria.fr/hal-01895099
  • 21B. Cessac, P. Kornprobst, M. Benzi, I. Caugant, D. Karvouniari, E. Kartsaki, S. Souihel.

    Biovision project-team: Biological vision: integrative models and vision aid systems for visually impaired people, October 2018, Fête de la science, Poster.

    https://hal.inria.fr/hal-01896505
  • 22R. Herzog, M.-J. Escobar, R. Cofré, A. Palacios, B. Cessac.

    Dimensionality Reduction on Spatio-Temporal Maximum Entropy Models of Spiking Networks, November 2018, working paper or preprint. [ DOI : 10.1101/278606 ]

    https://hal.inria.fr/hal-01917485
  • 23E. Kartsaki, B. Cessac, G. Hilgen, E. Sernagor.

    How specific classes of retinal cells contribute to vision: A Computational Model, June 2018, C@uca 2018 Meeting, Poster.

    https://hal.inria.fr/hal-01816921
  • 24D. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac.

    Pattern formation and criticality in the developing retina, June 2018, International Conference on Mathematical Neuroscience, Poster.

    https://hal.archives-ouvertes.fr/hal-01807929
  • 25S. Souihel, B. Cessac.

    A computational study of anticipation in the retina, September 2018, Bernstein conference 2018, Poster.

    https://hal.inria.fr/hal-01942516
  • 26S. Souihel, F. Chavane, O. Marre, B. Cessac.

    Processing various motion features and measuring RGCs pairwise correlations with a 2D retinal model, June 2018, International Conference on Mathematical Neuroscience ICMNS 2018, Poster.

    https://hal.inria.fr/hal-01809589
  • 27S. Souihel, F. Chavane, O. Marre, B. Cessac.

    Processing various motion features and measuring RGCs pairwise correlations with a 2D retinal model, June 2018, AREADNE 2018 Research in Encoding And Decoding of Neural Ensembles, Poster.

    https://hal.inria.fr/hal-01866259
References in notes
  • 28W. I. Al-Atabany, M. A. Memon, S. M. Downes, P. A. Degenaar.

    Designing and testing scene enhancement algorithms for patients with retina degenerative disorders., in: Biomedical engineering online, 2010, vol. 9, no 1, 27 p.
  • 29W. I. Al-Atabany, T. Tong, P. A. Degenaar.

    Improved content aware scene retargeting for retinitis pigmentosa patients, in: Biomedical engineering online, 2010, vol. 9, no 1.
  • 30F. M. Atay, S. Banisch, P. Blanchard, B. Cessac, E. Olbrich.

    Perspectives on Multi-Level Dynamics, in: The interdisciplinary journal of Discontinuity, Nonlinearity, and Complexity, 2016, vol. 5, pp. 313 - 339. [ DOI : 10.5890/DNC.2016.09.009 ]

    https://hal.inria.fr/hal-01387733
  • 31M. Auvray, E. Myin.

    Perception With Compensatory Devices: From Sensory Substitution to Sensorimotor Extension, in: Cognitive Science, 2009, vol. 33, no 6, pp. 1036–1058.

    http://dx.doi.org/10.1111/j.1551-6709.2009.01040.x
  • 32S. Avidan, A. Shamir.

    Seam Carving for Content-aware Image Resizing, in: ACM Trans. Graph., July 2007, vol. 26, no 3.

    http://doi.acm.org/10.1145/1276377.1276390
  • 33B. Cessac, R. Cofré.

    Spike train statistics and Gibbs distributions, in: Journal of Physiology-Paris, November 2013, vol. 107, no 5, pp. 360-368, Special issue: Neural Coding and Natural Image Statistics.

    http://hal.inria.fr/hal-00850155
  • 34Á. Csapó, G. Wersényi, H. Nagy, T. Stockman.

    A survey of assistive technologies and applications for blind users on mobile platforms: a review and foundation for research, in: Journal on Multimodal User Interfaces, 2015, vol. 9, no 4, pp. 275–286.

    http://dx.doi.org/10.1007/s12193-015-0182-7
  • 35M. Djilas, B. Kolomiets, L. Cadetti, H. Lorach, R. Caplette, S. Ieng, A. Rebsam, J. A. Sahel, R. Benosman, S. Picaud.

    Pharmacologically Induced Wave-Like Activity in the Adult Retina, in: ARVO Annual Meeting Abstract, March 2012.
  • 36S. I. Firth, C.-T. Wang, M. B. Feller.

    Retinal waves: mechanisms and function in visual system development, in: Cell Calcium, 2005, vol. 37, no 5, pp. 425 - 432, Calcium in the function of the nervous system: New implications. [ DOI : 10.1016/j.ceca.2005.01.010 ]

    http://www.sciencedirect.com/science/article/pii/S0143416005000278
  • 37K. J. Ford, M. B. Feller.

    Assembly and disassembly of a retinal cholinergic network, in: Visual Neuroscience, 2012, vol. 29, pp. 61–71. [ DOI : 10.1017/S0952523811000216 ]

    http://journals.cambridge.org/article_S0952523811000216
  • 38B. Froissard.

    Assistance visuelle des malvoyants par traitement d'images adaptatif, Université de Saint-Etienne, February 2014.
  • 39B. Froissard, H. Konik, E. Dinet.

    Digital content devices and augmented reality for assisting low vision people, in: Visually Impaired: Assistive Technologies, Challenges and Coping Strategies, Nova Science Publishers, December 2015.

    https://hal-ujm.archives-ouvertes.fr/ujm-01222251
  • 40E. Ganmor, R. Segev, E. Schneidman.

    Sparse low-order interaction network underlies a highly correlated and learnable neural population code, in: PNAS, 2011, vol. 108, no 23, pp. 9679-9684.
  • 41E. Ganmor, R. Segev, E. Schneidman.

    The architecture of functional interaction networks in the retina, in: The journal of neuroscience, 2011, vol. 31, no 8, pp. 3044-3054.
  • 42E. Jaynes.

    Information theory and statistical mechanics, in: Phys. Rev., 1957, vol. 106, 620 p.
  • 43H. Moshtael, T. Aslam, I. Underwood, B. Dhillon.

    High Tech Aids Low Vision: A Review of Image Processing for the Visually Impaired, in: Translational vision science & technology (TVST), 2015, vol. 4, no 4.
  • 44E. Peli, T. Peli.

    Image Enhancement For The Visually Impaired, in: Optical Engineering, 1984, vol. 23, no 1.

    https://doi.org/10.1117/12.7973251
  • 45E. Schneidman, M. Berry, R. Segev, W. Bialek.

    Weak pairwise correlations imply strongly correlated network states in a neural population, in: Nature, 2006, vol. 440, no 7087, pp. 1007–1012.
  • 46E. Sernagor, M. Hennig.

    Retinal Waves: Underlying Cellular Mechanisms and Theoretical Considerations, in: Cellular Migration and Formation of Neuronal Connections - Comprehensive Developmental Neuroscience, J. Rubenstein, P. Rakic (editors), Elsevier, 2012.
  • 47J. Shlens, G. Field, J. Gauthier, M. Grivich, D. Petrusca, A. Sher, A. Litke, E. Chichilnisky.

    The Structure of Multi-Neuron Firing Patterns in Primate Retina, in: Journal of Neuroscience, 2006, vol. 26, no 32, 8254 p.
  • 48G. Tkacik, O. Marre, T. Mora, D. Amodei, M. Berry, W. Bialek.

    The simplest maximum entropy model for collective behavior in a neural network, in: J Stat Mech, 2013, P03011 p.
  • 49J.-C. Vasquez, A. Palacios, O. Marre, M. J. Berry, B. Cessac.

    Gibbs distribution analysis of temporal correlations structure in retina ganglion cells, in: J. Physiol. Paris, May 2012, vol. 106, no 3-4, pp. 120-127.

    http://arxiv.org/abs/1112.2464
  • 50H. Winnemöller, J. E. Kyprianidis, S. C. Olsen.

    XDoG: An eXtended difference-of-Gaussians compendium including advanced image stylization, in: Computers & Graphics, October 2012, vol. 36, no 6, pp. 740–753, 2011 Joint Symposium on Computational Aesthetics (CAe), Non-Photorealistic Animation and Rendering (NPAR), and Sketch-Based Interfaces and Modeling (SBIM). [ DOI : doi:10.1016/j.cag.2012.03.004 ]
  • 51R. O. L. Wong, M. Meister, C. J. Shatz.

    Transient Period of Correlated Bursting Activity During Development of the Mammalian Retina, in: Neuron, November 1993, vol. 11, no 5, pp. 923–938.
  • 52H. Xu, T. Burbridge, M. Ye, X. Ge, Z. Zhou, M. Crair.

    Retinal Wave Patterns Are Governed by Mutual Excitation among Starburst Amacrine Cells and Drive the Refinement and Maintenance of Visual Circuits, in: The Journal of Neuroscience, 2016, vol. 36, no 13, pp. 3871-3886.
  • 53T. L. I. for Innovation in Vision Science.

    Chapter 7- Restoring Vision to the Blind: Advancements in Vision Aids for the Visually Impaired, in: Translational Vision Science & Technology, 2014, vol. 3, no 7, 9 p.

    http://dx.doi.org/10.1167/tvst.3.7.9


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