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PublicationsListBCessac

Publications Bruno Cessac

  1. S. Ebert, T. Buffet, B. S. Sermet, O. Marre, B. Cessac Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapses, Nature Communications volume 15, Article number: 6118 (2024) https://hal.inria.fr/hal-03939794/file/The_Role_of_Inhibitory_Synapses_in_Retinal_surprise_Coding-2.pdf BibTex
  2. E. Kartsaki, G. Hilgen, E. Sernagor, B. Cessac, “How does the inner retinal network shape the ganglion cells receptive field : a computational study“, Neural Computation, 2024, 36 (6), pp.1041-1083. ⟨10.1162/neco_a_01663⟩.
  3. B. Cessac, D. Matzakou-Karvouniari, The non linear dynamics of retinal waves Physica D: Nonlinear Phenomena, Elsevier,Volume 439, November 2022, 133436 https://hal.inria.fr/hal-03485137/file/main.pdf https://doi.org/10.1016/j.physd.2022.133436
  4. B. Cessac, Retinal processing: insights from mathematical modelling, Journal of Imaging, MDPI, 2022, Special Issue Mathematical Modeling of Human Vision and Its Application to Image Processing, 8 (1), pp.14. ⟨10.3390/jimaging8010014⟩
  5. Gerrit Hilgen, Evgenia Kartsaki, Viktoriia Kartysh, Bruno Cessac, Evelyne Sernagor, A novel approach to the functional classification of retinal ganglion cells Open Biology, Royal Society, ⟨10.1101/2021.05.09.443323⟩
  6. D. Pamplona, G. Hilgen, M. Hennig, B. Cessac, E. Sernagor, P. Kornprobst, “Receptive field estimation in large visual neuron assemblies using a super-resolution approach Journal of Neurophysiology, American Physiological Society, 2022, 127 (5), pp.1334–1347. ⟨10.1152/jn.00076.2021⟩
  7. B. Cessac, I. Ampuero, R. Cofre, Linear response for spiking neuronal networks with unbounded memory. Entropy, MDPI, 2021, 23 (2), pp.155. ⟨10.3390/e23020155⟩
  8. S. Souihel, B. Cessac, “On the potential role of lateral connectivity in retinal anticipation“, Journal of Mathematical Neuroscience, BioMed Central, 2021, 11, ⟨10.1186/s13408-020-00101-z⟩.
  9. B. Cessac, “The retina as a dynamical system”, in “Recent Trends in Chaotic, Nonlinear and Complex Dynamics”, World Scientific, J. Awrejcewicz, S. Rajasekar and M. Ragulskis Eds, 2020.
  10. R. Cofré, C. Maldonado, B. Cessac, “Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics“, Entropy 2020, 22, 1330.
  11. J. Vohryzek, G. Deco, B. Cessac, M. L. Kringelbach and J. Cabral,« Ghost attractors in spontaneous brain activity: wandering in a repertoire of functionally relevant BOLD phase-locking solutions », Frontiers in Systems Neuroscience, Frontiers, 2020, 14, ⟨10.3389/fnsys.2020.00020⟩.
  12. B. Cessac, Linear response in neuronal networks: from neurons dynamics to collective response, Chaos, American Institute of Physics, 2019, 29 (103105).
  13. D. Karvouniari, L. Gil, O. Marre, S. Picaud, B.Cessac. A biophysical model explains the spontaneous bursting behavior in the developing retina, Scientific Reports, Nature Publishing Group, 2019, 9 (1), pp.1-23.
  14. B. Cessac, The retina: a fascinating object of study for a physicist, Proceedings of the Complex Systems Academy of Excellence, Complex systems Nice, 2018.
  15. R. Herzog, M.-J. Escobar , A. G. Palacios, B. Cessac, Dimensionality Reduction and Reliable Observations in Maximum Entropy Models on Spiking Networks, BioArxiv, (2018).
  16. B. Cessac, P. Kornprobst, S. Kraria, H. Nasser, D. Pamplona, G. Portelli, T. Viéville  PRANAS: a new platform for retinal analysis and simulation,  Frontiers in Neuroinformatics, Vol 11, page 49, (2017).
  17. G. Hilgen, S. Pirmoradian, D. Pamplona, P. Kornprobst, B. Cessac, M. H. Hennig, and E. Sernagor.   Pan-retinal characterization of light responses from ganglion cells in the developing mouse retina. Scientific Reports, volume 7, Article number: 42330 (2017) .
  18. Bruno Cessac, Arnaud Le Ny, Eva Löcherbach. On the mathematical consequences of binning spike trains. Neural Computation, January 2017, Vol. 29, No. 1, Pages 146-170.
  19. Fatihcan M. Atay, Sven Banisch, Philippe Blanchard, Bruno Cessac, Eckehard Olbrich. Perspectives on Multi-Level Dynamics, Discontinuity, Nonlinearity, and Complexity, Vol. 5 (3) (2016).
  20. Rodrigo Cofré, Bruno Cessac, “Exact computation of the maximum-entropy potential of spiking neural-network models“, Phys. Rev. E 89, 052117 (2014).
  21. Hassan Nasser, Bruno Cessac, Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains, Entropy (2014), 16(4), 2244-2277; doi:10.3390/e16042244.
  22. Jeremie Naudé, Bruno Cessac, Hugues Berry, and Bruno Delord, “Effects of Cellular Homeostatic Intrinsic Plasticity on Dynamical and Computational Properties of Biological Recurrent Neural Networks” , The Journal of Neuroscience, 18 September 2013, 33(38): 15032-15043; doi: 10.1523/JNEUROSCI.0870-13. (2013).
  23. B. Cessac and R. Cofré, Spike train statistics and Gibbs distributions,  J. Physiol. Paris, Volume 107, Issue 5, Pages 360-368 (November 2013). Special issue: Neural Coding and Natural Image Statistics.
  24. Rodrigo Cofré and Bruno Cessac Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses, Chaos, Solitons & Fractals, Volume 50, May 2013, Pages 13-31.
  25. Hassan Nasser, Olivier Marre, and Bruno Cessac. Spike trains analysis using gibbs distributions and monte-carlo method”, J. Stat. Mech. (2013) P03006.
  26. B. Cessac A. Palacios. Spike train statistics from empirical facts to theory: the case of the retina, in “Modeling in Computational Biology and Biomedicine: A Multidisciplinary Endeavor”, F. CAZALS, P. KORNPROBST (editors), Lectures Notes in Mathematical and Computational Biology (LNMCB), Springer-Verlag, 2013.
  27. H. Rostro-Gonzalez, , B. Cessac, T. Viéville, “ Parameters estimation in spiking neural networks: a reverse-engineering approach », J. Neural Eng. 9 (2012) 026024.
  28. H. Rostro-Gonzalez, B. Cessac, B. Girau, C. Torres-Huitzil, “The role of the asymptotic dynamics in the design of FPGA-based hardware implementations of gIF-type neural networks”, J. Physiol. Paris,  vol. 105, n° 1–3, pages 91—97, (2011).
  29. J.C. Vasquez, A. Palacios, O. Marre, M.J. Berry II, B. Cessac, Gibbs distribution analysis of temporal correlation structure on multicell spike trains from retina ganglion cells,  J. Physiol. Paris, Volume 106, Issues 3–4, May–August 2012, Pages 120–127.
  30. Cessac, B (2011) Statistics of spike trains in conductance-based neural networks: Rigorous results, The Journal of Mathematical Neuroscience, 2011, 1:8 (2011).
  31. Cessac, B (2010) A discrete time neural network model with spiking neurons: II: Dynamics with noise. J Math Biol, Journal of Mathematical Biology: Volume 62, Issue 6 (2011), Page 863-900.
  32. B. Cessac, H. Paugam-Moisy, T. Viéville, “Overview of facts and issues about neural coding by spike”, J. Physiol., Paris, 104, (1-2), 5-18, (2010).
  33. B. Cessac, “Neural Networks as dynamical systems”, International Journal of Bifurcations and Chaos, Volume: 20, Issue: 6(2010) pp. 1585-1629     DOI: 10.1142/S0218127410026721.
  34. B. Cessac, H. Berry, “Du chaos dans les neurones”, Pour la Science, Novembre 2009.
  35. B. Cessac, H. Rostro, J.C. Vasquez, T. Viéville , “How Gibbs distributions may naturally arise from synaptic adaptation mechanisms”, J. Stat. Phys,136, (3), 565-602 (2009).
  36. O. Faugeras, J. Touboul, B. Cessac, “A constructive mean field analysis of multi population neural networks with random synaptic weights and stochastic inputs”,  Front. Comput. Neurosci. (2009) 3:1.
  37. B. Cessac, Viéville T., “On Dynamics of Integrate-and-Fire Neural Networks with Adaptive Conductances.”, Front. Comput. Neurosci. (2008) 2:2.
  38. Siri B., Berry H., Cessac B., Delord B., Quoy M., « A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks », Neural Comp., vol 20, num 12, (2008), pp 2937-2966.
  39. B. Cessac “A discrete time neural network model with spiking neurons. Rigorous results on the spontaneous dynamics”, J. Math. Biol., Volume 56, Number 3, 311-345 (2008).
  40. Siri, B., Quoy, M., Cessac, B., Delord, B. and Berry, H., “Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons”. Journal of Physiology (Paris),101(1-3):138-150 (2007).
  41. Cessac B., “Does the complex susceptibility of the Hénon map have a pole in the upper-half plane ? A numerical investigation.”, Nonlinearity, 20, 2883-2895 (2007).
  42. Samuelides M., Cessac B., “Random recurrent neural networks dynamics.”, EPJ Special Topics “Topics in Dynamical Neural Networks : From Large Scale Neural Networks to Motor Control and Vision”, Vol. 142, Num. 1, 7-88, (2007).
  43. Cessac B., Samuelides M., “From Neuron to Neural Networks dynamics. “, EPJ Special Topics “Topics in Dynamical Neural Networks : From Large Scale Neural Networks to Motor Control and Vision”, Vol. 142, Num. 1, 89-122, (2007).
  44. Cessac B., Dauce E., Perrinet L., Samuelides M., “Topics in dynamical neural networks – From large scale neural networks to motor control and vision – Introduction”, EPJ Special Topics, Vol. 142, Num 1,1-5, (2007).
  45. Cessac B., Sepulchre J.A., “Linear Response in a class of simple systems far from equilibrium”. , Physica D, Volume 225, Issue 1 , 13-28 (2006).
  46. Barber M., Blanchard Ph., Buchinger E., Cessac B., Streit L.,“A Luhmann-based model of communication, learning and innovation”, Journal of Artificial Societies and Social Simulation, Vol 9, Issue 4 (2006).
  47. Cessac B., Sepulchre J.A., “Transmitting a signal by amplitude modulation in a chaotic network'”, Chaos, 16, 013104, (2006).
  48. Cessac B., Sepulchre J.A., “Stable resonances and signal propagation in a chaotic network of coupled units”, Phys. Rev. E, 70, 056111 (2004).
  49. Cessac B., Blanchard Ph., Krüger T., Meunier J.L.,“Self-Organized Criticality and thermodynamic formalism”, Journal of Statistical Physics, Vol. 115, No 516, 1283-1326 (2004).
  50. Volchenkov D., Blanchard Ph.,Cessac B.,”Quantum field theory renormalization group approach to self-organized criticality: the case of random boundaries.”, International Journal of Modern Physics B, Vol. 16, No.8, 1171-1204, (2002).
  51. Cessac B., Meunier J.L., “Anomalous scaling and Lee-Yang zeros in Self-Organized Criticality.”, Phys. Rev. E, Vol (2002).
  52. Cessac B., Blanchard Ph.,Krüger T., “Lyapunov exponents and transport in the Zhang model of Self-Organized Criticality.”, Phys. Rev. E, Vol. 64, 016133, (2001).
  53. Blanchard Ph., Cessac B., Krüger T., “What can one learn about Self-Organized Critiality from Dynamical System theory ?”, Jour. of Stat. Phys., 98, 375-404, (2000).
  54. Dauce E., Quoy M., Cessac B., Doyon B. and Samuelides M. “Self-Organization and Dynamics reduction in recurrent networks: stimulus presentation andlearning”, Neural Networks, (11), 521-533, (1998). 
  55. Blanchard Ph., Cessac B. Krueger T.,”A dynamical system approach to SOC models of Zhang’s type.” J. of Stat. Phys., 88, 307-318, (1997).
  56. Samuelides M., Doyon B., Cessac B., Quoy M. “Spontaneous dynamics and associative learning in an asymmetric recurrent neural network”, Math. of Neural Networks, 312-317, (1996).
  57. Cessac B., “Increase in complexity in random neural networks”, J. de Physique I (France), 5, 409-432, (1995).
  58. Cessac B., “Occurence of chaos and AT line in random neural networks”, Europhys. Let., 26 (8), 577-582, (1994).
  59. Cessac B., “Absolute Stability criteria for random asymmetric neural networks”, J. of Physics A, 27, L927-L930, (1994).
  60. Cessac B., Doyon B., Quoy M., Samuelides M. “Mean-field equations, bifurcation map and route to chaos in discrete time neural networks”, Physica D, 74, 24-44(1994).
  61.  Doyon B., Cessac B., Quoy M., Samuelides M. “On bifurcations and chaos in random neural networks”, Acta Biotheoretica., Vol. 42, Num. 2/3, 215-225,(1994).
  62. Doyon B., Cessac B., Quoy M., Samuelides M. “Chaos in Neural Networks With Random Connectivity”, International Journal Of Bifurcation and Chaos, Vol. 3, Num. 2, 279-291 (1993).
  63. Quoy M., Cessac B., Doyon B., Samuelides M. “Dynamical behaviour of neural networks with discrete time dynamics”, Neural Network World, Vol. 3, Num. 6, 845-848 (1993).

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