Publications Bruno Cessac
- 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)
- 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⟩.
- B. Cessac, D. Matzakou-Karvouniari, The non linear dynamics of retinal waves Physica D: Nonlinear Phenomena, Elsevier,Volume 439, November 2022, 133436 https://doi.org/10.1016/j.physd.2022.133436
- 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⟩
- 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⟩
- 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⟩
- 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⟩
- 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⟩.
- 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.
- R. Cofré, C. Maldonado, B. Cessac, “Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics“, Entropy 2020, 22, 1330.
- 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⟩.
- B. Cessac, Linear response in neuronal networks: from neurons dynamics to collective response, Chaos, American Institute of Physics, 2019, 29 (103105).
- 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.
- B. Cessac, The retina: a fascinating object of study for a physicist, Proceedings of the Complex Systems Academy of Excellence, Complex systems Nice, 2018.
- R. Herzog, M.-J. Escobar , A. G. Palacios, B. Cessac, Dimensionality Reduction and Reliable Observations in Maximum Entropy Models on Spiking Networks, BioArxiv, (2018).
- 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).
- 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) .
- 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.
- Fatihcan M. Atay, Sven Banisch, Philippe Blanchard, Bruno Cessac, Eckehard Olbrich. Perspectives on Multi-Level Dynamics, Discontinuity, Nonlinearity, and Complexity, Vol. 5 (3) (2016).
- Rodrigo Cofré, Bruno Cessac, “Exact computation of the maximum-entropy potential of spiking neural-network models“, Phys. Rev. E 89, 052117 (2014).
- 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.
- 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).
- 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.
- 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.
- Hassan Nasser, Olivier Marre, and Bruno Cessac. Spike trains analysis using gibbs distributions and monte-carlo method”, J. Stat. Mech. (2013) P03006.
- 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.
- H. Rostro-Gonzalez, , B. Cessac, T. Viéville, “ Parameters estimation in spiking neural networks: a reverse-engineering approach », J. Neural Eng. 9 (2012) 026024.
- 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).
- 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.
- Cessac, B (2011) Statistics of spike trains in conductance-based neural networks: Rigorous results, The Journal of Mathematical Neuroscience, 2011, 1:8 (2011).
- 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.
- 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).
- 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.
- B. Cessac, H. Berry, “Du chaos dans les neurones”, Pour la Science, Novembre 2009.
- 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).
- 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.
- B. Cessac, Viéville T., “On Dynamics of Integrate-and-Fire Neural Networks with Adaptive Conductances.”, Front. Comput. Neurosci. (2008) 2:2.
- 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.
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- Cessac B., Sepulchre J.A., “Transmitting a signal by amplitude modulation in a chaotic network'”, Chaos, 16, 013104, (2006).
- Cessac B., Sepulchre J.A., “Stable resonances and signal propagation in a chaotic network of coupled units”, Phys. Rev. E, 70, 056111 (2004).
- 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).
- 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).
- Cessac B., Meunier J.L., “Anomalous scaling and Lee-Yang zeros in Self-Organized Criticality.”, Phys. Rev. E, Vol (2002).
- 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).
- 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).
- 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).
- 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).
- 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).
- Cessac B., “Increase in complexity in random neural networks”, J. de Physique I (France), 5, 409-432, (1995).
- Cessac B., “Occurence of chaos and AT line in random neural networks”, Europhys. Let., 26 (8), 577-582, (1994).
- Cessac B., “Absolute Stability criteria for random asymmetric neural networks”, J. of Physics A, 27, L927-L930, (1994).
- 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).
- 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).
- 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).
- 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).