| Year |
Citation |
Score |
| 2022 |
Cessac B. Retinal Processing: Insights from Mathematical Modelling. Journal of Imaging. 8. PMID 35049855 DOI: 10.3390/jimaging8010014 |
0.317 |
|
| 2021 |
Cessac B, Ampuero I, Cofré R. Linear Response of General Observables in Spiking Neuronal Network Models. Entropy (Basel, Switzerland). 23. PMID 33514033 DOI: 10.3390/e23020155 |
0.776 |
|
| 2020 |
Cofré R, Maldonado C, Cessac B. Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics. Entropy (Basel, Switzerland). 22. PMID 33266513 DOI: 10.3390/e22111330 |
0.743 |
|
| 2020 |
Vohryzek J, Deco G, Cessac B, Kringelbach ML, Cabral J. Ghost Attractors in Spontaneous Brain Activity: Recurrent Excursions Into Functionally-Relevant BOLD Phase-Locking States. Frontiers in Systems Neuroscience. 14: 20. PMID 32362815 DOI: 10.3389/Fnsys.2020.00020 |
0.375 |
|
| 2019 |
Cessac B. Linear response in neuronal networks: From neurons dynamics to collective response. Chaos (Woodbury, N.Y.). 29: 103105. PMID 31675822 DOI: 10.1063/1.5111803 |
0.443 |
|
| 2019 |
Matzakos-Karvouniari D, Gil L, Orendorff E, Marre O, Picaud S, Cessac B. A biophysical model explains the spontaneous bursting behavior in the developing retina. Scientific Reports. 9: 1859. PMID 30755684 DOI: 10.1038/S41598-018-38299-4 |
0.356 |
|
| 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 |
0.67 |
|
| 2016 |
Cessac B, Le Ny A, Löcherbach E. On the Mathematical Consequences of Binning Spike Trains. Neural Computation. 1-25. PMID 27764593 DOI: 10.1162/Neco_A_00898 |
0.397 |
|
| 2016 |
Atay FM, Banisch S, Blanchard P, Cessac B, Olbrich E, Volchenkov D. Perspectives on Multi-Level Dynamics The Interdisciplinary Journal of Discontinuity, Nonlinearity, and Complexity. 5: 313-339. DOI: 10.5890/Dnc.2016.09.009 |
0.361 |
|
| 2015 |
Pamplona D, Hilgen G, Cessac B, Sernagor E, Kornprobst P. A super-resolution approach for receptive fields estimation of neuronal ensembles Bmc Neuroscience. 16. DOI: 10.1186/1471-2202-16-S1-P130 |
0.376 |
|
| 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 |
0.539 |
|
| 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 |
0.445 |
|
| 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 |
0.549 |
|
| 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 |
0.477 |
|
| 2013 |
Muratori M, Cessac B. Beyond dynamical mean-field theory of neural networks Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P60 |
0.491 |
|
| 2013 |
Taouali W, Cessac B. A maximum likelihood estimator of neural network synaptic weights Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P59 |
0.501 |
|
| 2013 |
Nasser H, Kraria S, Cessac B. EnaS: a new software for neural population analysis in large scale spiking networks Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P57 |
0.512 |
|
| 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 |
0.535 |
|
| 2013 |
Cofré R, Cessac B. Dynamics and spike trains statistics in conductance-based integrate-and-fire neural networks with chemical and electric synapses Chaos, Solitons & Fractals. 50: 13-31. DOI: 10.1016/j.chaos.2012.12.006 |
0.41 |
|
| 2013 |
Cofre R, Cessac B. Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses Bmc Neuroscience. 14. DOI: 10.1016/J.Chaos.2012.12.006 |
0.574 |
|
| 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 |
0.75 |
|
| 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 |
0.408 |
|
| 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. |
0.682 |
|
| 2011 |
Cessac B. Statistics of spike trains in conductance-based neural networks: Rigorous results. Journal of Mathematical Neuroscience. 1: 8. PMID 22657160 DOI: 10.1186/2190-8567-1-8 |
0.555 |
|
| 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 |
0.448 |
|
| 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 |
0.536 |
|
| 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 |
0.572 |
|
| 2010 |
Cessac B. A view of neural networks as dynamical systems International Journal of Bifurcation and Chaos. 20: 1585-1629. DOI: 10.1142/S0218127410026721 |
0.559 |
|
| 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 |
0.478 |
|
| 2009 |
Rostro-Gonzalez H, Cessac B, Vasquez JC, Viéville T. Back-engineering of spiking neural networks parameters Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P289 |
0.762 |
|
| 2009 |
Vasquez JC, Cessac B, Rostro-Gonzalez H, Vieville T. How Gibbs distributions may naturally arise from synaptic adaptation mechanisms Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P213 |
0.555 |
|
| 2009 |
Cessac B, Viéville T. Parametric estimation of spike train statistics Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P165 |
0.728 |
|
| 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 |
0.752 |
|
| 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 |
0.77 |
|
| 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 |
0.5 |
|
| 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 |
0.536 |
|
| 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 |
0.503 |
|
| 2007 |
Cessac B, Viéville T. Revisiting time discretisation of spiking network models Bmc Neuroscience. 8. DOI: 10.1186/1471-2202-8-S2-P76 |
0.777 |
|
| 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 |
0.74 |
|
| 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 |
0.726 |
|
| 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 |
0.657 |
|
| 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 |
0.46 |
|
| 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 |
0.326 |
|
| 2004 |
Cessac B, Blanchard P, Krüger T, Meunier JL. Self-Organized Criticality and thermodynamic formalism Journal of Statistical Physics. 115: 1283-1326. DOI: 10.1023/B:Joss.0000028057.16662.89 |
0.316 |
|
| 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. DOI: 10.1023/A:1018639308981 |
0.367 |
|
| 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 |
0.697 |
|
| 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. DOI: 10.1007/Bf02508473 |
0.375 |
|
| 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 |
0.706 |
|
| 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 |
0.343 |
|
| 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 |
0.37 |
|
| 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 |
0.719 |
|
| 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 |
0.722 |
|
| 1993 |
DOYON B, CESSAC B, QUOY M, SAMUELIDES M. CONTROL OF THE TRANSITION TO CHAOS IN NEURAL NETWORKS WITH RANDOM CONNECTIVITY International Journal of Bifurcation and Chaos. 3: 279-291. DOI: 10.1142/S0218127493000222 |
0.727 |
|
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