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Bibliography

Major publications by the team in recent years
  • 1M. Benzi, M.-J. Escobar, P. Kornprobst.

    A Bio-inspired Synergistic Virtual Retina Model for Tone Mapping, in: Computer Vision and Image Understanding, December 2017. [ DOI : 10.1016/j.cviu.2017.11.013 ]

    https://hal.inria.fr/hal-01655814
  • 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.
  • 3B. Cessac, P. Kornprobst, S. Kraria, H. Nasser, D. Pamplona, G. Portelli, T. Vieville.

    PRANAS: A New Platform for Retinal Analysis and Simulation, in: Frontiers in Neuroinformatics, September 2017, vol. 11, 49 p.

    https://hal.inria.fr/hal-01588737
  • 4R. 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.
  • 5R. Cofré, B. Cessac.

    Exact computation of the maximum-entropy potential of spiking neural-network models, in: Phys. Rev. E, 2014, vol. 89, no 052117.
  • 6M.-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
  • 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, no 1. [ DOI : 10.3389/neuro.10.001.2010 ]

    http://arxiv.org/abs/0808.1113
  • 8D. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac.

    A biophysical model explains the oscillatory behaviour of immature starburst amacrine cells, March 2017, submitted to Scientific Reports.

    https://hal.inria.fr/hal-01484133
  • 9T. 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
  • 10N. 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
  • 11J. 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
  • 12J. 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
  • 13A. 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

  • 14N. V. K. Medathati.

    Towards synergistic models of motion information processing in biological and artificial vision, UCA, Inria, December 2017.

    https://hal.inria.fr/tel-01577041

Articles in International Peer-Reviewed Journals

  • 15M. Benzi, M.-J. Escobar, P. Kornprobst.

    A Bio-inspired Synergistic Virtual Retina Model for Tone Mapping, in: Computer Vision and Image Understanding, December 2017, pp. 1-27. [ DOI : 10.1016/j.cviu.2017.11.013 ]

    https://hal.inria.fr/hal-01655814
  • 16B. Cessac, P. Kornprobst, S. Kraria, H. Nasser, D. Pamplona, G. Portelli, T. Vieville.

    PRANAS: A New Platform for Retinal Analysis and Simulation, in: Frontiers in Neuroinformatics, September 2017, vol. 11, 49 p.

    https://hal.inria.fr/hal-01588737
  • 17B. Cessac, A. Le Ny, E. Löcherbach.

    On the mathematical consequences of binning spike trains, in: Neural Computation, January 2017, vol. 29, no 1, pp. 146-170.

    https://hal.inria.fr/hal-01351964
  • 18A. Drogoul, R. Veltz.

    Hopf bifurcation in a nonlocal nonlinear transport equation stemming from stochastic neural dynamics, in: Chaos, February 2017. [ DOI : 10.1063/1.4976510 ]

    https://hal.inria.fr/hal-01412154
  • 19G. Hilgen, S. Pirmoradian, D. Pamplona, P. Kornprobst, B. Cessac, M. H. Hennig, E. Sernagor.

    Pan-retinal characterisation of Light Responses from Ganglion Cells in the Developing Mouse Retina, in: Scientific Reports, February 2017, vol. 7.

    https://hal.inria.fr/hal-01589946
  • 20N. V. K. Medathati, J. Rankin, A. I. Meso, P. Kornprobst, G. S. Masson.

    Recurrent network dynamics reconciles visual motion segmentation and integration, in: Scientific Reports, September 2017, vol. 7, 11270 p. [ DOI : 10.1038/s41598-017-11373-z ]

    https://hal.inria.fr/hal-01589893

Invited Conferences

  • 21B. Cessac.

    Gibbs distribution: from neural network dynamics to spike train statistics estimation, in: Advanced theoretical approaches to collective network phenomena: Bernstein Conference Satellite Workshop, Goettingen, Germany, September 2017.

    https://hal.inria.fr/hal-01626784
  • 22B. Cessac.

    Handling spatio-temporal correlations in neuronal systems, in: LACONEU 2017 - Computational Neuroscience Summer School, Valparaiso, Chile, January 2017.

    https://hal.inria.fr/hal-01626754
  • 23B. Cessac.

    Statistical analysis of retinal responses, in: Random Structures on the Brain 2017, Leiden, Netherlands, December 2017, pp. 1-122.

    https://hal.inria.fr/hal-01644408
  • 24D. Karvouniari, L. Gil, O. Marre, B. Cessac.

    Multi scale dynamics in retinal waves, in: Brain Dynamics on Multiple Scales - Paradigms, their Relations, and Integrated Approaches, Dresde, Germany, June 2017.

    https://hal.inria.fr/hal-01626779
  • 25D. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac.

    Multi scale dynamics in retinal waves , in: C@UCA 2017 Meeting, Fréjus, France, June 2017.

    https://hal.inria.fr/hal-01626772

International Conferences with Proceedings

  • 26B. Cessac, D. Karvouniari, L. Gil.

    Multi scale dynamics in retinal waves, in: Winter School on Deterministic and Stochastic Models in Neuroscience, Toulous, Toulouse, France, December 2017, pp. 1-89.

    https://hal.inria.fr/hal-01644404
  • 27M. Chessa, A. Patino-Saucedo, H. Rostro, E. Castet, F. Solari, P. Kornprobst.

    Real-time image enhancement in virtual reality applications for low vision people, in: Vision 2017, the 12th International Conference by the International Society for Low Vision Research and Rehabilitation (ISLRR), La Hague, Netherlands, June 2017.

    https://hal.inria.fr/hal-01589975
  • 28N. S. Kartheek Medathati, M. S. Chessa, G. S. Masson, P. Kornprobst, F. S. Solari.

    Adaptive Motion Pooling and Diffusion for Optical Flow Computation, in: WBICV 2017 : First International Workshop on Brain-Inspired Computer Vision, Catania, Sicily, Italy, September 2017.

    https://hal.inria.fr/hal-01589983

Conferences without Proceedings

  • 29B. Cessac, D. Karvouniari, L. Gil.

    Multi scale dynamics in retinal waves, in: 2 nd Systems Biology meeting at Sorbonne University, Paris, France, December 2017, pp. 1-89.

    https://hal.inria.fr/hal-01644398

Internal Reports

  • 30M. Benzi, M.-J. U. Escobar, P. Kornprobst.

    A Bio-inspired Synergistic Virtual Retina Model for Tone Mapping, Inria Sophia Antipolis, February 2017, no RR-9033, 30 p.

    https://hal.inria.fr/hal-01478391
  • 31B. Cessac, P. Kornprobst, S. Kraria, H. Nasser, D. Pamplona, G. Portelli, T. Viéville.

    PRANAS: A new platform for retinal analysis and simulation, Inria Sophia Antipolis ; Inria Bordeaux Sud-Ouest, August 2017, no RR-8958, 27 p.

    https://hal.inria.fr/hal-01377307
  • 32N. V. K. Medathati, J. Rankin, A. I. Meso, P. Kornprobst, G. S. Masson.

    Recurrent network dynamics reconciles visual motion segmentation and integration, Inria Sophia Antipolis, March 2017, no RR-9041, 28 p.

    https://hal.inria.fr/hal-01482294

Other Publications

  • 33B. Cessac, R. Cofre.

    Linear Response of General Observables in Spiking Neuronal Network Models, November 2017, 25 pages, 2 figures.

    https://hal.inria.fr/hal-01626840
  • 34B. Cessac, D. Karvouniari.

    A mathematical approach to retinal waves, January 2017, Lecture.

    https://hal.inria.fr/cel-01626745
  • 35R. Herzog, M.-J. Escobar, A. Palacios, B. Cessac.

    Dimensionality Reduction on Maximum Entropy Models on Spiking Networks, November 2017, working paper or preprint.

    https://hal.inria.fr/hal-01649063
  • 36D. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac.

    A biophysical model explains the oscillatory behaviour of immature starburst amacrine cells, November 2017, 25 pages, 15 figures, submitted.

    https://hal.inria.fr/hal-01484133
  • 37D. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac.

    Following stage II retinal waves during development with a biophysical model , October 2017, International retina meeting, Poster.

    https://hal.inria.fr/hal-01638100
  • 38D. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac.

    Following stage II retinal waves during development with a biophysical model : A biophysical model for retinal waves , September 2017, 1 p, Bernstein Conference 2017, Poster.

    https://hal.inria.fr/hal-01638098
  • 39S. Souihel, B. Cessac.

    How does the retina anticipate the motion of complex shapes ?, September 2017, Bernstein conférence, Poster.

    https://hal.inria.fr/hal-01638102
  • 40S. Souihel, B. Cessac.

    Modifying a biologically inspired retina simulator to reconstruct realistic responses to moving stimuli, June 2017, Conference Cauca, Poster.

    https://hal.inria.fr/hal-01638104
  • 41S. Souihel, B. Cessac.

    Motion processing in the retina, November 2017, GDR multielectrodes , Poster.

    https://hal.inria.fr/hal-01638105
References in notes
  • 42C. Aguilar, E. Castet.

    Gaze-contingent simulation of retinopathy: some potential pitfalls and remedies, in: Vision Research, 2011, vol. 51, pp. 997–1012.
  • 43W. Al-Atabany, B. McGovern, K. Mehran, R. Berlinguer-Palmini, P. Degenaar.

    A Processing Platform for Optoelectronic/Optogenetic Retinal Prosthesis, in: IEEE Transactions on Biomedical Engineering, March 2013, vol. 60, no 3, pp. 781–791.
  • 44W. 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.
  • 45W. 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.
  • 46F. 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
  • 47M. 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
  • 48S. 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
  • 49J.-B. Bernard, A. Calabrèse, E. Castet.

    Role of syllable segmentation processes in peripheral word recognition, in: Vision Research, 2014, vol. 105, pp. 226–232.
  • 50J.-B. Bernard, A.-C. Scherlen, E. Castet.

    Page mode reading with simulated scotomas: A modest effect of interline spacing on reading speed, in: Vision Research, 2007, vol. 47, pp. 3447–3459.
  • 51B. 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
  • 52M. Chessa, N. Noceti, F. Odone, F. Solari, J. Sosa-García, L. Zini.

    An integrated artificial vision framework for assisting visually impaired users, in: Computer Vision and Image Understanding, Special issue on Assistive Computer Vision and Robotics - Assistive Solutions for Mobility, Communication and HMI, August 2016, vol. 149, pp. 209–228.
  • 53Á. 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
  • 54M. 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.
  • 55D. Elliott, M. Trukolo-Ilic, J. Strong, R. Pace, A. Plotkin, P. Bevers.

    Demographic characteristics of the vision-disabled elderly, in: Investigative Ophthalmology & Visual Science, November 1997, vol. 38, no 12, pp. 2566–75.
  • 56S. 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
  • 57K. 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
  • 58B. Froissard.

    Assistance visuelle des malvoyants par traitement d'images adaptatif, Université de Saint-Etienne, February 2014.
  • 59B. 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
  • 60E. 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.
  • 61E. 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.
  • 62E. Jain, Y. Sheikh, A. Shamir, J. Hodgins.

    Gaze-driven Video Re-editing, in: ACM Transactions on Graphics, February 2015, vol. 34, no 2.
  • 63E. Jaynes.

    Information theory and statistical mechanics, in: Phys. Rev., 1957, vol. 106, 620 p.
  • 64Y. H. Lee, G. Medioni.

    RGB-D camera based wearable navigation system for visually impaired, in: Computer Vision and Image Understanding, Special issue on Assistive Computer Vision and Robotics - "Assistive Solutions for Mobility, Communication and HMI", August 2016, vol. 149, pp. 3–20.

    http://www.sciencedirect.com/science/article/pii/S1077314216000692
  • 65G. Legge.

    Prentice medal lecture 2013: visual accessibility: a challenge for low-vision research, in: Optom Vis Sci., 2014, vol. 91, no 7, pp. 696–706.
  • 66T. Luft, C. Colditz, O. Deussen.

    Image Enhancement by Unsharp Masking the Depth Buffer, in: ACM Transactions on Graphics, 2006, vol. 25, no 3, pp. 1206–1213. [ DOI : 10.1145/1141911.1142016 ]

    http://graphics.uni-konstanz.de/publikationen/Luft2006ImageEnhancementUnsharp
  • 67G. Maiello, M. Chessa, F. Solari, P. J. Bex.

    Simulated disparity and peripheral blur interact during binocular fusion, in: Journal of Vision, 2014, vol. 14, no 8, 13 p.

    http://dx.doi.org/10.1167/14.8.13
  • 68H. 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.
  • 69E. Peli, R. B. Goldstein, G. M. Young, C. L. Trempe, S. M. Buzney.

    Image Enhancement for the Visually Impaired, in: Investigative Ophthalmology & Visual Science, July 1991, vol. 32, no 8, pp. 2337–2350.
  • 70A.-C. Scherlen, J.-B. Bernard, A. Calabrese, E. Castet.

    Page mode reading with simulated scotomas: Oculo-motor patterns, in: Vision Research, 2008, pp. 1870–1878.
  • 71E. 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.
  • 72E. Sernagor, M. Hennig.

    1, in: Retinal Waves: Underlying Cellular Mechanisms and Theoretical Considerations, J. Rubenstein, P. Rakic (editors), Elsevier, 2012.
  • 73J. 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.
  • 74G. 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.
  • 75J.-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
  • 76A. 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.
  • 77R. 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.
  • 78H. 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.
  • 79T. 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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