Publications

Bruno Cessac

International peer reviews.

  1. H. Rostro-Gonzalez, , B. Cessac, T. Viéville, “ Parameter estimation in spiking neural networks: a reverse-engineering approach », J. Neural Eng. 9 (2012) 026024.
  2. The role of the asymptotic dynamics in the design of FPGA-based hardware implementations of gIF-type neural networks, Horacio Rostro-Gonzalez, Bruno Cessac, Bernard Girau, Cesar Torres-Huitzil, J. Physiol. Paris,  vol. 105, n° 1–3, pages 91—97, (2011).
  3. 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 (2012), in press.
  4. Cessac, B (2011) Statistics of spike trains in conductance-based neural networks: Rigorous results, The Journal of Mathematical Neuroscience 2011, 1:8 (2011).
  5. 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.
  6. 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).
  7. 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. 
  8. 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). 
  9. 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.
  10. B. Cessac, Viéville T., ``On Dynamics of Integrate-and-Fire Neural Networks with Adaptive Conductances.'', Front. Comput. Neurosci. (2008) 2:2.
  11. 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.
  12. 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).
  13. 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).
  14. 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).
  15. 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).
  16. 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).
  17. 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). 
  18. 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).
  19. 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).
  20. Cessac B., Sepulchre J.A., "Transmitting a signal by amplitude modulation in a chaotic network'", Chaos 16, 013104, (2006).
  21. Cessac B., Sepulchre J.A., ``Stable resonances and signal propagation in a chaotic network of coupled units'', Phys. Rev. E 70, 056111 (2004).
  22. 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).
  23. 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).
  24. Cessac B., Meunier J.L., "Anomalous scaling and Lee-Yang zeros in Self-Organized Criticality.", Phys. Rev. E, Vol (2002).
  25. 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).
  26. 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).
  27. 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). 
  28. 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).
  29. 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).
  30. Cessac B., "Increase in complexity in random neural networks", J. de Physique I (France), 5, 409-432, (1995).
  31. Cessac B., "Occurence of chaos and AT line in random neural networks", Europhys. Let., 26 (8), 577-582, (1994). 
  32. Cessac B., "Absolute Stability criteria for random asymmetric neural networks", J. of Physics A, 27, L927-L930, (1994).
  33. 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).
  34. 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).
  35. 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). 
  36. 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).

Proceedings in International peer conferences.

  1. J.C. Vasquez, B. Cessac, and T. Viéville., "New approaches to spike train analysis and neuronal coding", CNS-2011 workshop on 27/28 Jul 2011.
  2. J.C. Vasquez, B. Cessac, and T. Viéville. Entropy-based parametric estimation of spike train statistics Statistical Mechanics of Learning and Inference, Stockholm-Mariehanm, May 2010. 
  3. T. Viéville, B. Cessac, "Parametric Estimation of spike train statistics", CNS 09 Berlin.
  4. H. Rostro-Gonzalez, B. Cessac, J.C. Vasquez and T. Vieville. Back-engineering in spiking neural networks parameters. Eighteenth Annual Cmputational Neuroscience Meeting CNS 2009. July 18th-23rd 2009, Berlin, Germany. BMC Neuroscience 2009, 10(Suppl.10):P289, BioMed Central.
  5. J. C. Vasquez, B. Cessac, H. Rostro-Gonzalez, T. Viéville, "How Gibbs Distributions may naturally arise from synaptic adaptation mechanism", CNS 09, Berlin.
  6. Faugeras O., Touboul J., Cessac B., “A constructive mean-field analysis of multi population neural networks with random synaptic weights”, COSYNE 09.
  7. Siri, B., Berry, H., Cessac, B., Delord, B. and Quoy, M., ``Local learning rules and bifurcations in the global dynamics of random recurrent neural networks''. European Conference on Complex Systems (ECCS'07), October, Dresden, Germany, (2007).
  8. B. Cessac, Thierry Viéville, ``Revisiting time discretisation of spiking network models'', from Sixteenth Annual Computational Neuroscience Meeting : CNS*2007 Toronto, Canada. 7-12 July 2007.-BMC Neuroscience 2007, 8(Suppl 2) :P76 doi:10.1186/1471-2202-8-S2-P76.
  9. Siri, B., Berry, H., Cessac, B., Delord, B. and Quoy, M. ``Topological and dynamical structures induced by Hebbian learning in random neural networks''. In International Conference on Complex Systems, ICCS 2006, Boston, MA, USA, June 2006.
  10. B. Cessac, O. Mazet, M. Samuelides, H. Soula, "Mean field theory for random recurrent spiking neural networks", NOLTA'05 (Non Linear Theory and its Applications) October 18-21, 2005, Brugge, Belgium.
  11. Cessac B., Blanchard Ph., Volchenkov D.,``Does renormaisation group help very much in Self-Organized criticality '', Proceedings of ``The science of complexity: from mathématics to technology to a sustainable world '', Bielefeld 2002.
  12. Doyon B., Cessac B., Quoy M., Samuelides M., "Destabilization and route to chaos in neural networks with random connectivity", Neural Information and Processing Systems: Natural and Synthetics, (1992).

Proceedings in French conferences.
  1. Bruno Cessac, Hassan Nasser, Juan-Carlos Vasquez, Spike trains statistics in Integrate and Fire Models: exact result,  NeuroComp2010 (Lyon).
  2. J.C. Vasquez, Hassan Nasser, Adrian Palacios, Bruno Cessac, Thierry Vieville and Horacio Rostro-Gonzalez. Parametric estimation of Spike train statistics by Gibbs distributions : an application to bio-inspired and experimetal data. Neurocomp 2010 (Lyon).
  3. B. Cessac, H. Rostro, J.C. Vasquez, T. Viéville, "Statistics of spikes trains, synaptic plasticity and Gibbs distributions", proceedings of the conference NeuroComp 2008 (Marseille).
  4. B. Cessac, H. Rostro, J.C. Vasquez, T. Viéville, "To which extend is the ``neural code'' a metric ?", proceedings of the conference NeuroComp 2008 (Marseille).
  5. Siri, B., Berry, H., Cessac, B., Delord, B., Quoy, M., and Temam, O. , ``Learning-induced topological effects on dynamics in neural networks''. In NeuroComp'06:206--209, Pont-à-Mousson, France, 23-24 October 2006.
  6. Cessac B., Blanchard Ph., Volchenkov D.,``Does renormaisation group help very much in Self-Organized criticality '', Proceedings of ``The science of complexity: from mathématics to technology to a sustainable world '', Bielefeld 2002.
  7. Cessac B., "Some fractal aspects of Self-Organized Criticality.". Proceedings du colloque "Fractales en progrès" , en l'honneur du 80ème anniversaire de Benoit Mandelbrot,12-13 Novembre 2004.
  8. Cessac B., Blanchard Ph., Krüger T., "A dynamical system approach to Self-Organized Criticality", "Proceedings of the International Conference on Mathematical Results in Statistical Mechanics", 27-31 Juillet 1998, Marseille.
Book chapters.
  1. B. Cessac and A. Palacios, "Spike train statistics from empirical facts to theory: the case of the retina", In Mathematical Problems in Computational Biology and Biomedicine, F. Cazals and P. Kornprobst, Springer, to appear.
Vulgarisation papers.
  1. B. Cessac, H. Berry, "Du chaos dans les neurones", Pour la Science, Novembre 2009.
  2. B. Cessac, "Vrai ou faux ? Grâce à la simulation, on peut tout prédire", interstices, 04-05 (2009)
  3. B. Cessac, T. Viéville, C. Leininger, "Le cerveau est-il un bon modèle de réseau de neurones ?" , "Interstices", 11-07, (2007).
INRIA research reports.
  1. Parametric Estimation of Gibbs distributions as generalized maximum-entropy models for the analysis of spike train statistics. Vasquez J. C., Viéville T., Cessac B. N° RR-7561 (2011) [inria-00574954 - version 2]
  2. Reverse-engineering in spiking neural networks parameters: exact deterministic parameters estimation Rostro-Gonzalez H., Vasquez J.-C., Cessac B., Viéville T. N° RR-7199 (2010) [inria-00455415