Bruno Cessac - Publications

Affiliations: 
INRIA Sophia Antipolis, Biot, Provence-Alpes-Côte d'Azur, France 
Website:
http://www.inln.cnrs.fr/rubrique.php3?id_rubrique=40

41 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2017 Cessac B, Kornprobst P, Kraria S, Nasser H, Pamplona D, Portelli G, Viéville T. PRANAS: A New Platform for Retinal Analysis and Simulation. Frontiers in Neuroinformatics. 11: 49. PMID 28919854 DOI: 10.3389/fninf.2017.00049  0.44
2017 Hilgen G, Pirmoradian S, Pamplona D, Kornprobst P, Cessac B, Hennig MH, Sernagor E. Pan-retinal characterisation of Light Responses from Ganglion Cells in the Developing Mouse Retina. Scientific Reports. 7: 42330. PMID 28186129 DOI: 10.1038/srep42330  0.4
2014 Cofré R, Cessac B. Exact computation of the maximum-entropy potential of spiking neural-network models. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 89: 052117. PMID 25353749 DOI: 10.1103/PhysRevE.89.052117  1
2014 Nasser H, Cessac B. Parameter estimation for spatio-temporal maximum entropy distributions application to neural spike trains Entropy. 16: 2244-2277. DOI: 10.3390/e16042244  1
2013 Naudé J, Cessac B, Berry H, Delord B. Effects of cellular homeostatic intrinsic plasticity on dynamical and computational properties of biological recurrent neural networks. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 33: 15032-43. PMID 24048833 DOI: 10.1523/JNEUROSCI.0870-13.2013  1
2013 Cessac B, Cofré R. Spike train statistics and Gibbs distributions. Journal of Physiology, Paris. 107: 360-8. PMID 23501168 DOI: 10.1016/j.jphysparis.2013.03.001  1
2013 Nasser H, Marre O, Cessac B. Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method Journal of Statistical Mechanics: Theory and Experiment. 2013. DOI: 10.1088/1742-5468/2013/03/P03006  1
2013 Cessac B, Palacios AG. Spike train statistics from empirical facts to theory: The case of the retina Modeling in Computational Biology and Biomedicine: a Multidisciplinary Endeavor. 2147483647: 261-302. DOI: 10.1007/978-3-642-31208-3_8  1
2012 Rostro-Gonzalez H, Cessac B, Vieville T. Parameter estimation in spiking neural networks: a reverse-engineering approach. Journal of Neural Engineering. 9: 026024. PMID 22419215 DOI: 10.1088/1741-2560/9/2/026024  1
2012 Vasquez JC, Marre O, Palacios AG, Berry MJ, Cessac B. Gibbs distribution analysis of temporal correlations structure in retina ganglion cells. Journal of Physiology, Paris. 106: 120-7. PMID 22115900 DOI: 10.1016/j.jphysparis.2011.11.001  1
2012 Cessac B, Salas R, Viéville T. Using event-based metric for event-based neural network weight adjustment Esann 2012 Proceedings, 20th European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning. 591-596.  1
2011 Rostro-Gonzalez H, Cessac B, Girau B, Torres-Huitzil C. The role of the asymptotic dynamics in the design of FPGA-based hardware implementations of gIF-type neural networks. Journal of Physiology, Paris. 105: 91-7. PMID 21964248 DOI: 10.1016/j.jphysparis.2011.09.004  1
2011 Cessac B. A discrete time neural network model with spiking neurons: II: dynamics with noise. Journal of Mathematical Biology. 62: 863-900. PMID 20658138 DOI: 10.1007/s00285-010-0358-4  1
2010 Cessac B, Paugam-Moisy H, Viéville T. Overview of facts and issues about neural coding by spikes. Journal of Physiology, Paris. 104: 5-18. PMID 19925865 DOI: 10.1016/j.jphysparis.2009.11.002  1
2010 Cessac B. A view of neural networks as dynamical systems International Journal of Bifurcation and Chaos. 20: 1585-1629. DOI: 10.1142/S0218127410026721  1
2009 Faugeras O, Touboul J, Cessac B. A constructive mean-field analysis of multi-population neural networks with random synaptic weights and stochastic inputs. Frontiers in Computational Neuroscience. 3: 1. PMID 19255631 DOI: 10.3389/neuro.10.001.2009  1
2009 Cessac B, Rostro H, Vasquez JC, Viéville T. How Gibbs distributions may naturally arise from synaptic adaptation mechanisms. A model-based argumentation Journal of Statistical Physics. 136: 565-602. DOI: 10.1007/s10955-009-9786-1  1
2008 Cessac B, Viéville T. On dynamics of integrate-and-fire neural networks with conductance based synapses. Frontiers in Computational Neuroscience. 2: 2. PMID 18946532 DOI: 10.3389/neuro.10.002.2008  1
2008 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 Computation. 20: 2937-66. PMID 18624656 DOI: 10.1162/neco.2008.05-07-530  1
2008 Cessac B. A discrete time neural network model with spiking neurons. Rigorous results on the spontaneous dynamics. Journal of Mathematical Biology. 56: 311-45. PMID 17874106 DOI: 10.1007/s00285-007-0117-3  1
2007 Siri B, Quoy M, Delord B, Cessac B, Berry H. Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons. Journal of Physiology, Paris. 101: 136-48. PMID 18042357 DOI: 10.1016/j.jphysparis.2007.10.003  1
2007 Samuelides M, Cessac B. Random recurrent neural networks dynamics European Physical Journal: Special Topics. 142: 89-122. DOI: 10.1140/epjst/e2007-00059-1  1
2007 Cessac B, Samuelides M. From neuron to neural networks dynamics European Physical Journal: Special Topics. 142: 7-88. DOI: 10.1140/epjst/e2007-00058-2  1
2007 Cessac B, Daucé E, Perrinet L, Samuelides M. Introduction European Physical Journal: Special Topics. 142: 1-5. DOI: 10.1140/epjst/e2007-00057-3  1
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. DOI: 10.1088/0951-7715/20/12/007  1
2007 Cessac B, Sepulchre JA. Linear response, susceptibility and resonances in chaotic toy models Physica D: Nonlinear Phenomena. 225: 13-28. DOI: 10.1016/j.physd.2006.09.034  1
2006 Cessac B, Sepulchre JA. Transmitting a signal by amplitude modulation in a chaotic network. Chaos (Woodbury, N.Y.). 16: 013104. PMID 16599735 DOI: 10.1063/1.2126813  1
2006 Barber M, Blanchard P, Buchinger E, Cessac B, Streit L. Expectation-driven interaction: A model based on Luhmann's contingency approach Jasss. 9.  1
2004 Cessac B, Sepulchre JA. Stable resonances and signal propagation in a chaotic network of coupled units. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 70: 056111. PMID 15600696 DOI: 10.1103/PhysRevE.70.056111  1
2004 Cessac B, Blanchard P, Krüger T, Meunier JL. Self-Organized Criticality and thermodynamic formalism Journal of Statistical Physics. 115: 1283-1326.  1
2002 Cessac B, Meunier JL. Anomalous scaling and Lee-Yang zeros in self-organized criticality. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 65: 036131. PMID 11909189 DOI: 10.1103/PhysRevE.65.036131  1
2002 Volchenkov D, Blanchard PH, Cessac B. Quantum field theory renormalization group approach to self-organized critical models: The case of random boundaries International Journal of Modern Physics B. 16: 1171-1204. DOI: 10.1142/S0217979202010130  1
2001 Cessac B, Blanchard P, Krüger T. Lyapunov exponents and transport in the Zhang model of self-organized criticality. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 64: 016133. PMID 11461357  1
2000 Blanchard P, Cessac B, Krüger T. What can one learn about self-organized criticality from dynamical systems theory? Journal of Statistical Physics. 98: 375-404.  1
1998 Samuelides M, Doyon B, Cessac B, Quoy M, Dauce E. Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning. Neural Networks : the Official Journal of the International Neural Network Society. 11: 521-533. PMID 12662827 DOI: 10.1016/S0893-6080(97)00131-7  1
1997 Blanchard P, Cessac B, Krüger T. A dynamical system approach to SOC models of Zhang's type Journal of Statistical Physics. 88: 307-318.  1
1995 Doyon B, Cessac B, Quoy M, Samuelides M. Mean-field equations, bifurcation map and chaos in discrete time, continuous state, random neural networks. Acta Biotheoretica. 43: 169-75. PMID 7709685 DOI: 10.1007/BF00709441  1
1994 Cessac B. Occurrence of chaos and AT line in random neural networks Epl. 26: 577-582. DOI: 10.1209/0295-5075/26/8/004  1
1994 Cessac B. Absolute stability criterion for discrete time neural networks Journal of Physics a: Mathematical and General. 27: L927-L930. DOI: 10.1088/0305-4470/27/24/004  1
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: Nonlinear Phenomena. 74: 24-44. DOI: 10.1016/0167-2789(94)90024-8  1
1994 Doyon B, Cessac B, Quoy M, Samuelides M. On bifurcations and chaos in random neural networks Acta Biotheoretica. 42: 215-225. DOI: 10.1007/BF00709492  1
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