EN FR
EN FR


previous Previous | up Home


Bibliography

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

    A discrete time neural network model with spiking neurons II. Dynamics with noise, in: J. Math. Biol., 2011, vol. 62, pp. 863-900.
  • 2B. 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
  • 3B. Cessac, P. Kornprobst, S. Kraria, H. Nasser, D. Pamplona, G. Portelli, T. Viéville.

    ENAS: A new software for spike train analysis and simulation, Inria Sophia Antipolis ; Inria Bordeaux Sud-Ouest, October 2016, no RR-8958.

    https://hal.inria.fr/hal-01377307
  • 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
  • 8T. 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
  • 9N. V. K. Medathati, H. Neumann, G. S. Masson, P. Kornprobst.

    Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision, Inria Sophia Antipolis, May 2016, no 8698, 71 p, To appear in CVIU.

    https://hal.inria.fr/hal-01131645
  • 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
  • 13J.-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
  • 14A. 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

Articles in International Peer-Reviewed Journals

  • 15F. 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
  • 16B. Cessac, A. Le Ny, E. Löcherbach.

    On the mathematical consequences of binning spike trains, in: Neural Computation, August 2016.

    https://hal.inria.fr/hal-01351964
  • 17T. 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
  • 18N. 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
  • 19A. I. Meso, J. S. Rankin, O. S. Faugeras, P. Kornprobst, G. S. Masson.

    The relative contribution of noise and adaptation to competition during tri-stable motion perception, in: Journal of Vision, October 2016.

    https://hal.inria.fr/hal-01383118
  • 20G. Portelli, J. M. Barrett, G. Hilgen, T. Masquelier, A. Maccione, S. Di Marco, L. Berdondini, P. Kornprobst, E. Sernagor.

    Rank order coding: a retinal information decoding strategy revealed by large-scale multielectrode array retinal recordings, in: eNeuro, May 2016. [ DOI : 10.1523/ENEURO.0134-15.2016 ]

    https://hal.inria.fr/hal-01316105

International Conferences with Proceedings

  • 21A. Drogoul, G. Aubert, B. Cessac, P. Kornprobst.

    A new nonconvex variational approach for sensory neurons receptive field estimation, in: 6th International Workshop on New Computational Methods for Inverse Problems, Cachan, France, IOPScience, May 2016, vol. 756, no 1, 12006. [ DOI : 10.1088/1742-6596/756/1/012006 ]

    https://hal.archives-ouvertes.fr/hal-01379952
  • 22D. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac.

    Mathematical and experimental studies on retinal waves, in: 2nd International Conference on Mathematical Neuroscience (ICMNS), Juan les Pins, France, May 2016.

    https://hal.inria.fr/hal-01324365

Internal Reports

  • 23B. Cessac, P. Kornprobst, S. Kraria, H. Nasser, D. Pamplona, G. Portelli, T. Viéville.

    ENAS: A new software for spike train analysis and simulation, Inria Sophia Antipolis ; Inria Bordeaux Sud-Ouest, October 2016, no RR-8958.

    https://hal.inria.fr/hal-01377307
  • 24A. Drogoul, G. Aubert, B. Cessac, P. Kornprobst.

    A nonconvex variational approach for receptive field estimation, Inria Sophia Antipolis, August 2016, no 8837, 42 p.

    https://hal.inria.fr/hal-01279999
  • 25G. 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 , Institute of Neuroscience, Newcastle University ; Institute for Adaptive and Neural Computation, University of Edinburgh ; Inria, Neuromathcomp Team, June 2016. [ DOI : 10.1101/050393 ]

    https://hal.inria.fr/hal-01393525
  • 26N. V. K. Medathati, H. Neumann, G. S. Masson, P. Kornprobst.

    Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision, Inria Sophia Antipolis, May 2016, no 8698, 71 p, To appear in CVIU.

    https://hal.inria.fr/hal-01131645

Other Publications

  • 27B. Cessac, P. Kornprobst, S. Kraria, H. Nasser, D. Pamplona, G. Portelli, T. Vieville.

    ENAS: A new software for spike train analysis and simulation, September 2016, Bernstein conference, Poster.

    https://hal.inria.fr/hal-01368757
  • 28R. Cofré, B. Cessac.

    Spike train analysis and Gibbs distributions, September 2016, Bernstein Conference 2016, Poster.

    https://hal.inria.fr/hal-01378001
  • 29A. Drogoul, G. Aubert, B. Cessac, P. Kornprobst.

    A variational approach for receptive field estimation in the nonconvex case, May 2016, Second International Conference on Mathematical NeuroScience (ICMNS), Poster.

    https://hal.inria.fr/hal-01292700
  • 30A. Drogoul, R. Veltz.

    Evidence for Hopf bifurcation in a nonlocal nonlinear transport equation stemming from stochastic neural dynamics, December 2016, working paper or preprint.

    https://hal.inria.fr/hal-01412154
  • 31R. Herzog, M.-J. Escobar, A. Palacios, B. Cessac.

    Dimensionality Reduction in spatio-temporal MaxEnt models and analysis of Retinal Ganglion Cell Spiking Activity in experiments, September 2016, Bernstein Conference 2016 , Poster.

    https://hal.archives-ouvertes.fr/hal-01377370
  • 32D. 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
  • 33D. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac.

    Classifying the spatiotemporal patterns within stage II retinal waves through dynamical systems analysis, September 2016, Bernstein Conference 2016, Poster.

    https://hal.inria.fr/hal-01371596
  • 34D. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac.

    Modeling the emergence of stage II retinal waves in immature retina, June 2016, AREADNE 2016 Research in Encoding And Decoding of Neural Ensembles, Poster.

    https://hal.inria.fr/hal-01339875
  • 35N. V. K. Medathati, A. I. Meso, G. S. Masson, P. Kornprobst, J. Rankin.

    Understanding the impact of recurrent interactions on population tuning: Application to MT cells characterization, June 2016, AREADNE: Research in Encoding And Decoding of Neural Ensembles, Poster.

    https://hal.inria.fr/hal-01377606
  • 36S. Souihel.

    Motion anticipation in a Virtual Retina, Ecole des mines de Saint Etienne ; Inria Sophia Antipolis, October 2016.

    https://hal.inria.fr/hal-01399940
References in notes
  • 37W. 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.
  • 38W. 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.
  • 39W. 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.
  • 40S. 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
  • 41G. 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.
  • 42L. Berdondini, K. Imfeld, A. Maccione, M. Tedesco, S. Neukom, M. Koudelka-Hep, S. Martinoia.

    Active pixel sensor array for high spatio-temporal resolution electrophysiological recordings from single cell to large scale neuronal networks, in: Lab on a Chip, 2009, vol. 9, no 18, pp. 2644–2651.
  • 43M. Bertalmío.

    Image Processing for Cinema, CRC Press, 2014.
  • 44B. 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
  • 45B. 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
  • 46M. 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.
  • 47G. Dagnelie.

    Virtual technologies aid in restoring sight to the blind, in: Communications Through Virtual Technology: Identity Community and Technology in the Internet Age, Amsterdam, G. Riva, F. Davide (editors), IOS Press, 2001, chap. 15.
  • 48M. 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.
  • 49G. Eilertsen, R. Wanat, R. Mantiuk, J. Unger.

    Evaluation of Tone Mapping Operators for HDR-Video, in: Computer Graphics Forum, October 2013, vol. 32, no 7, pp. 275–284.
  • 50M. Fagan, C. Kilmon, V. Pandey.

    Exploring the adoption of a virtual reality simulation: The role of perceived ease of use, perceived usefulness and personal innovativeness, in: Campus-Wide Information Systems, 2012, vol. 29, no 2, pp. 117–127. [ DOI : 10.1108/10650741211212368 ]
  • 51E. Ferrea, A. Maccione, L. Medrihan, T. Nieus, D. Ghezzi, P. Baldelli, F. Benfenati, L. Berdondini.

    Large-scale, high-resolution electrophysiological imaging of field potentials in brain slices with microelectronic multielectrode arrays, in: Front. Neural Circuits, 2012, vol. 6, no November, 80 p. [ DOI : 10.3389/fncir.2012.00080 ]

    http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3496908&tool=pmcentrez&rendertype=abstract
  • 52S. 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
  • 53K. 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
  • 54B. Froissard.

    Assistance visuelle des malvoyants par traitement d'images adaptatif, Université de Saint-Etienne, February 2014.
  • 55B. 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
  • 56E. 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.
  • 57E. 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.
  • 58L. Horne, J. Alvarez, C. McCarthy, M. Salzmann, N. Barnes.

    Semantic labeling for prosthetic vision, 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. 113–125.

    http://www.sciencedirect.com/science/article/pii/S1077314216000692
  • 59E. Jain, Y. Sheikh, A. Shamir, J. Hodgins.

    Gaze-driven Video Re-editing, in: ACM Transactions on Graphics, February 2015, vol. 34, no 2.
  • 60E. Jaynes.

    Information theory and statistical mechanics, in: Phys. Rev., 1957, vol. 106, 620 p.
  • 61J. Kung, H. Yamaguchi, C. Liu, G. Johnson, M. Fairchild.

    Evaluating HDR rendering algorithms, in: ACM Transactions on Applied Perception, July 2007, vol. 4, no 2.
  • 62Y. 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
  • 63A. Maccione, M. H. Hennig, M. Gandolfo, O. Muthmann, J. Coppenhagen, S. J. Eglen, L. Berdondini, E. Sernagor.

    Following the ontogeny of retinal waves: pan-retinal recordings of population dynamics in the neonatal mouse, in: The Journal of physiology, 2014, vol. 592, no 7, pp. 1545–1563.
  • 64G. 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
  • 65K. 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
  • 66T. 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
  • 67A. Mohemmed, G. Lu, N. Kasabov.

    Evaluating SPAN Incremental Learning for Handwritten Digit Recognition, in: Neural Information Processing, Berlin, Heidelberg, Springer Berlin Heidelberg, 2012, pp. 670–677.
  • 68H. Nasser, B. Cessac.

    Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains, in: Entropy, 2014, vol. 16, no 4, pp. 2244-2277. [ DOI : 10.3390/e16042244 ]
  • 69H. Nasser, O. Marre, B. Cessac.

    Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method, in: Journal of Statistical Mechanics: Theory and Experiment, 2013, vol. 2013, P03006 p. [ DOI : doi:10.1088/1742-5468/2013/03/P03006 ]

    http://lanl.arxiv.org/abs/1209.3886
  • 70L. Paninski.

    Convergence properties of three spike-triggered analysis techniques, in: Network: Comput. Neural Syst., 2003, vol. 14, 437—464 p.
  • 71N. Parikh, L. Itti, J. Weiland.

    Saliency-based image processing for retinal prostheses, in: Journal of Neural Engineering, January 2010, vol. 7, no 1, pp. 016006–10. [ DOI : 10.1088/1741-2560/7/1/016006 ]
  • 72J. 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, 10.1007/s10827-012-0409-5. [ DOI : 10.1007/s10827-012-0409-5 ]

    http://hal.inria.fr/hal-00845593
  • 73E. 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.
  • 74E. Sernagor, M. Hennig.

    1, in: Retinal Waves: Underlying Cellular Mechanisms and Theoretical Considerations., J. Rubenstein, P. Rakic (editors), Elsevier, 2012.
  • 75J. 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.
  • 76K. D. Simmons, J. S. Prentice, G. Tkacik, J. Homann, H. K. Yee, S. E. Palmer, P. C. Nelson, V. Balasubramanian.

    Transformation of stimulus correlations by the retina., in: PLoS Comput. Biol., December 2013, vol. 9, no 12, e1003344 p. [ DOI : 10.1371/journal.pcbi.1003344 ]

    http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3854086&tool=pmcentrez&rendertype=abstract
  • 77G. Tkacik, O. Marre, D. Amodei, E. Schneidman, M. J. Bialek.

    Searching for collective behavior in a large network of sensory neurons., in: PLoS Comput. Biol., January 2014, vol. 10, no 1, e1003408 p. [ DOI : 10.1371/journal.pcbi.1003408 ]

    http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3879139&tool=pmcentrez&rendertype=abstract
  • 78G. 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.
  • 79P. 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. [ DOI : 10.1109/IJCNN.2015.7280759 ]
  • 80M. Vidne, Y. Ahmadian, J. Shlens, J. W. Pillow, J. Kulkarni, A. M. Litke, E. Chichilnisky, E. Simoncelli, L. Paninski.

    Modeling the impact of common noise inputs on the network activity of retinal ganglion cells., in: J. Comput. Neurosci., August 2012, vol. 33, no 1, pp. 97–121. [ DOI : 10.1007/s10827-011-0376-2 ]

    http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3560841&tool=pmcentrez&rendertype=abstract
  • 81B. Völgyi, F. Pan, D. L. Paul, J. T. Wang, A. D. Huberman, S. a. Bloomfield.

    Gap Junctions Are Essential for Generating the Correlated Spike Activity of Neighboring Retinal Ganglion Cells, in: PLoS One, 2013, vol. 8, no 7. [ DOI : 10.1371/journal.pone.0069426 ]
  • 82L.-K. Wong, K.-L. Low.

    Saliency retargeting: An approach to enhance image aesthetics, in: 2011 IEEE Workshop on Applications of Computer Vision (WACV), IEEE, 2011, pp. 73–80. [ DOI : 10.1109/WACV.2011.5711486 ]
  • 83R. 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.
  • 84H. 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.
  • 85J. Zheng, S. Lee, Z. J. Zhou.

    A transient network of intrinsically bursting starburst cells underlies the generation of retinal waves, in: Nat Neurosci, 2006, vol. 9, no 3, pp. 363-371.
  • 86S. Zordan, M. Zanotto, T. Niels, S. Di Marco, H. Amin, A. Maccione, L. Berdondini.

    A scalable high performance client/server framework to manage and analyze high dimensional datasets recorded by 4096 CMOS-MEAs, in: 7th International IEEE/EMBS Conference on Neural Engineering (NER), 2015. [ DOI : 10.1109/NER.2015.7146787 ]
  • 87D. van Krevelen, R. Poelman.

    A Survey of Augmented Reality Technologies, Applications and Limitations, in: The International Journal of Virtual Reality, 2010, vol. 9, no 2, pp. 1–20.


previous Previous | up Home