Sobolev spaces, Pure and Applied Mathematics, vol.140, 2003. ,
Dynamics of Neuronal Firing Correlation : Modulation of " Effective Connectivity, Journal of Neurophysiology, vol.61, pp.900-917, 1989. ,
Tests d'indépendance par bootstrap et permutation : étude asymptotique et nonasymptotique . Application en neurosciences ,
Bootstrap and permutation tests of independence for point processes, The Annals of Statistics, vol.43, issue.6, pp.2537-2564 ,
DOI : 10.1214/15-AOS1351SUPP
URL : https://hal.archives-ouvertes.fr/hal-01001984
Bayesian entropy estimation for binary spike train data using parametric prior knowledge, Advances in Neural Information Processing Systems, pp.1700-1708, 2013. ,
Non-parametric kernel estimation for symmetric Hawkes processes. Application to high frequency financial data, The European Physical Journal B, vol.96, issue.5, pp.1-12, 2012. ,
DOI : 10.1140/epjb/e2012-21005-8
URL : https://hal.archives-ouvertes.fr/hal-01313844
Scaling limits for Hawkes processes and application to financial statistics, 2012. ,
DOI : 10.1016/j.spa.2013.04.007
Modeling and predicting popularity dynamics of microblogs using self-excited Hawkes processes. arXiv preprint, 2015. ,
Single Units and Sensation: A Neuron Doctrine for Perceptual Psychology?, Perception, vol.1, issue.2, pp.371-394, 1972. ,
DOI : 10.1068/p010371
Controlling the false discovery rate : a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society. Series B. Methodological, vol.57, issue.1, pp.289-300, 1995. ,
Convergence of probability measures Wiley Series in Probability and Statistics : Probability and Statistics, 1999. ,
Finding Frequent Patterns in Parallel Point Processes, Advances in Intelligent Data Analysis XII, pp.116-126, 2013. ,
DOI : 10.1007/978-3-642-41398-8_11
Markov processes and parabolic partial differential equations. Encyclopedia of Quantitative Finance, pp.1142-1159, 2010. ,
URL : https://hal.archives-ouvertes.fr/inria-00193883
A markovian growth-collapse model, Advances in applied probability, pp.221-243, 2006. ,
DOI : 10.1017/s0001867800000884
URL : http://repository.tue.nl/597674
Point processes and queues, 1981. ,
DOI : 10.1007/978-1-4684-9477-8
Stability of nonlinear Hawkes processes, Ann. Probab, vol.24, issue.3, pp.1563-1588, 1996. ,
Identification of synaptic interactions, Biological Cybernetics, vol.12, issue.4, pp.213-228, 1976. ,
DOI : 10.1007/BF00365087
The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis, Neural Computation, vol.18, issue.2, pp.325-346, 2002. ,
DOI : 10.1038/17605
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.9977
Multiple neural spike train data analysis: state-of-the-art and future challenges, Nature Neuroscience, vol.7, issue.5, pp.456-461, 2004. ,
DOI : 10.1038/nn1228
Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons, Journal of Computational Neuroscience, vol.8, issue.3, pp.183-208, 2000. ,
DOI : 10.1023/A:1008925309027
Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates, Neural Computation, vol.15, issue.7, pp.1621-1671, 1999. ,
DOI : 10.1038/373612a0
Analysis of Nonlinear Noisy Integrate&Fire Neuron Models: blow-up and steady states, The Journal of Mathematical Neuroscience, vol.1, issue.1, 2011. ,
DOI : 10.1007/s11118-008-9093-5
A numerical solver for a nonlinear Fokker???Planck equation representation of neuronal network dynamics, Journal of Computational Physics, vol.230, issue.4, pp.1084-1099, 2011. ,
DOI : 10.1016/j.jcp.2010.10.027
Beyond blow-up in excitatory integrate and fire neuronal networks: Refractory period and spontaneous activity, Journal of Theoretical Biology, vol.350, pp.81-89, 2014. ,
DOI : 10.1016/j.jtbi.2014.02.005
Measure Solutions for Some Models in Population Dynamics, Acta Applicandae Mathematicae, vol.48, issue.3, pp.141-156, 2013. ,
DOI : 10.1007/s10440-012-9758-3
Mean-field limit of generalized Hawkes processes. arXiv preprint, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01217407
Microscopic approach of a time elapsed neural model, Mathematical Models and Methods in Applied Sciences, vol.25, issue.14, pp.252669-2719, 2015. ,
DOI : 10.1142/S021820251550058X
URL : https://hal.archives-ouvertes.fr/hal-01159215
Detection of dependence patterns with delay, Biometrical Journal, vol.26, issue.6, pp.1110-1130, 2015. ,
DOI : 10.1002/bimj.201400235
URL : https://hal.archives-ouvertes.fr/hal-00998864
Maximum likelihood identification of neural point process systems, Biological Cybernetics, vol.80, issue.4-5, pp.265-275, 1988. ,
DOI : 10.1080/01621459.1985.10477119
Limit theorems for some branching measure-valued processes, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00598030
Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model, Cerebral Cortex, vol.10, issue.9, pp.910-923, 2000. ,
DOI : 10.1093/cercor/10.9.910
A piecewise deterministic model for prey-predator communities. arXiv preprint, 2015. ,
Robust dynamic classes revealed by measuring the response function of a social system, Proceedings of the National Academy of Sciences, pp.15649-15653, 2008. ,
DOI : 10.1073/pnas.0803685105
Stochastic equations in infinite dimensions, 2014. ,
An introduction to the theory of point processes Elementary theory and methods, Probability and its Applications, 2003. ,
An introduction to the theory of point processes, II. Probability and its Applications General theory and structure, 2008. ,
Piecewise-deterministic Markov processes, Journal of the Royal Statistical Society. Series B (Methodological), pp.353-388, 1984. ,
DOI : 10.1007/978-1-4899-4483-2_2
Theoretical neuroscience, 2001. ,
Particle systems with a singular meanfield self-excitation. application to neuronal networks, Stochastic Processes and their Applications, pp.2451-2492, 2015. ,
DOI : 10.1016/j.spa.2015.01.007
URL : https://hal.archives-ouvertes.fr/hal-01001716
Global solvability of a networked integrate-and-fire model of McKean???Vlasov type, The Annals of Applied Probability, vol.25, issue.4, pp.2096-2133, 2015. ,
DOI : 10.1214/14-AAP1044
URL : https://hal.archives-ouvertes.fr/hal-00747565
Hawkes processes on large networks, The Annals of Applied Probability, vol.26, issue.1, pp.216-261, 2016. ,
DOI : 10.1214/14-AAP1089
URL : https://hal.archives-ouvertes.fr/hal-01259910
Multi-class oscillating systems of interacting neurons. arXiv preprint, 2015. ,
DOI : 10.1016/j.spa.2016.09.013
URL : http://doi.org/10.1016/j.spa.2016.09.013
Estimation in the partially observed stochastic Morris???Lecar neuronal model with particle filter and stochastic approximation methods, The Annals of Applied Statistics, vol.8, issue.2, pp.674-702, 2014. ,
DOI : 10.1214/14-AOAS729
URL : https://hal.archives-ouvertes.fr/hal-00712331
Synchrony and the binding problem in macaque visual cortex, Journal of Vision, vol.8, issue.7, p.30, 2008. ,
DOI : 10.1167/8.7.30
Statistical estimation of a growth-fragmentation model observed on a genealogical tree, Bernoulli, vol.21, issue.3, pp.1760-1799, 2015. ,
DOI : 10.3150/14-BEJ623
URL : https://hal.archives-ouvertes.fr/hal-01102799
Asymptotic description of stochastic neural networks. I. Existence of a large deviation principle, Comptes Rendus Mathematique, vol.352, issue.10, pp.841-846, 2014. ,
DOI : 10.1016/j.crma.2014.08.018
URL : https://hal.archives-ouvertes.fr/hal-01074836
A constructive mean-field analysis of multi population neural networks with random synaptic weights and stochastic inputs, Frontiers in Computational Neuroscience, vol.3, 2009. ,
DOI : 10.3389/neuro.10.001.2009
URL : https://hal.archives-ouvertes.fr/inria-00258345
A Hilbertian approach for fluctuations on the McKean-Vlasov model. Stochastic Process, Appl, vol.71, issue.1, pp.33-53, 1997. ,
On the rate of convergence in wasserstein distance of the empirical measure. Probability Theory and Related Fields, pp.1-32, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-00915365
On a toy model of interacting neurons. arXiv preprint, 2014. ,
The two-sample problem for Poisson processes: Adaptive tests with a nonasymptotic wild bootstrap approach, The Annals of Statistics, vol.41, issue.3, pp.1431-1461, 2013. ,
DOI : 10.1214/13-AOS1114SUPP
URL : https://hal.archives-ouvertes.fr/hal-00679102
Infinite Systems of Interacting Chains with Memory of Variable Length???A Stochastic Model for Biological Neural Nets, Journal of Statistical Physics, vol.5, issue.2, pp.896-921, 2013. ,
DOI : 10.1007/s10955-013-0733-9
Modeling networks of spiking neurons as interacting processes with memory of variable length, 2015. ,
Representation of cooperative firing activity among simultaneously recorded neurons, Journal of Neurophysiology, vol.54, issue.6, pp.1513-1528, 1985. ,
Simultaneously Recorded Trains of Action Potentials: Analysis and Functional Interpretation, Science, vol.164, issue.3881, pp.828-830, 1969. ,
DOI : 10.1126/science.164.3881.828
Coherence Stability and Effect of Random Natural Frequencies in Populations of Coupled Oscillators, Journal of Dynamics and Differential Equations, vol.63, issue.2, pp.333-367, 2014. ,
DOI : 10.1007/s10884-014-9370-5
URL : https://hal.archives-ouvertes.fr/hal-01018542
Transitions in Active Rotator Systems: Invariant Hyperbolic Manifold Approach, SIAM Journal on Mathematical Analysis, vol.44, issue.6, pp.4165-4194, 2012. ,
DOI : 10.1137/110846452
URL : https://hal.archives-ouvertes.fr/hal-00783567
Statistical models based on counting processes, 1997. ,
Spike synchronization and firing rate in a population of motor cortical neurons in relation to movement direction and reaction time, Biological Cybernetics, vol.88, issue.5, pp.360-373, 2003. ,
DOI : 10.1007/s00422-002-0385-3
Unitary joint events in multiple neuron spiking activity : detection, significance, and interpretation, 1996. ,
Data-Driven Significance Estimation for Precise Spike Correlation, Journal of Neurophysiology, vol.101, issue.3, pp.1126-1140, 2009. ,
DOI : 10.1152/jn.00093.2008
Unitary Events in Multiple Single-Neuron Spiking Activity: I. Detection and Significance, Neural Computation, vol.13, issue.10, pp.43-80, 2002. ,
DOI : 10.1016/0006-8993(90)90558-S
Unitary Events in Multiple Single-Neuron Spiking Activity: II. Nonstationary Data, Neural Computation, vol.18, issue.10, pp.81-119, 2002. ,
DOI : 10.1038/373515a0
Detecting unitary events without discretization of time, Journal of Neuroscience Methods, vol.94, issue.1, pp.67-79, 1999. ,
DOI : 10.1016/S0165-0270(99)00126-0
FADO: A Statistical Method to Detect Favored or Avoided Distances between Occurrences of Motifs using the Hawkes' Model, Statistical Applications in Genetics and Molecular Biology, vol.4, issue.1, 2005. ,
DOI : 10.2202/1544-6115.1119
Lasso and probabilistic inequalities for multivariate point processes, Bernoulli, vol.21, issue.1, pp.83-143, 2015. ,
DOI : 10.3150/13-BEJ562
URL : https://hal.archives-ouvertes.fr/hal-00722668
A Rate and History-Preserving Resampling Algorithm for Neural Spike Trains, Neural Computation, vol.21, issue.2, pp.1244-1258, 2009. ,
DOI : 10.1152/jn.00644.2004
Spectra of some self-exciting and mutually exciting point processes, Biometrika, vol.58, issue.1, pp.83-90, 1971. ,
DOI : 10.1093/biomet/58.1.83
A cluster process representation of a self-exciting process, Journal of Applied Probability, pp.493-503, 1974. ,
The Organization of Behavior : A Neuropsychological Theory, 1949. ,
Abstract, Advances in Applied Probability, vol.22, issue.01, 2014. ,
DOI : 10.1017/S0143385700000924
A quantitative description of membrane current and its application to conduction and excitation in nerve, The Journal of Physiology, vol.117, issue.4, pp.500-544, 1952. ,
DOI : 10.1113/jphysiol.1952.sp004764
Statistical analysis and modelling of spatial point patterns, 2008. ,
DOI : 10.1002/9780470725160
Mean-Field Limit of a Stochastic Particle System Smoothly Interacting Through Threshold Hitting-Times and Applications to Neural Networks with Dendritic Component, SIAM Journal on Mathematical Analysis, vol.47, issue.5, pp.3884-3916, 2015. ,
DOI : 10.1137/140989042
URL : https://hal.archives-ouvertes.fr/hal-01069398
Limit theorems for stochastic processes, of Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences, 2003. ,
DOI : 10.1007/978-3-662-02514-7
Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process, Journal of Computational Neuroscience, vol.21, issue.11, pp.563-579, 2011. ,
DOI : 10.1007/s10827-011-0326-z
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3232348
Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process, Journal of Computational Neuroscience, vol.21, issue.11, pp.31563-579, 2011. ,
DOI : 10.1007/s10827-011-0326-z
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3232348
Limit theorems for nearly unstable hawkes processes. The Annals of Applied Probability, pp.600-631, 2015. ,
DOI : 10.1214/14-aap1005
URL : https://hal.archives-ouvertes.fr/hal-01138784
Weak convergence of sequences of semimartingales with applications to multitype branching processes, Advances in Applied Probability, vol.94, issue.01, pp.20-65, 1986. ,
DOI : 10.2307/3212499
Propagation of chaos and fluctuations for a moderate model with smooth initial data, Annales de l'IHP Probabilités et statistiques, pp.727-766, 1998. ,
DOI : 10.1016/S0246-0203(99)80002-8
Cumulants of Hawkes point processes, Physical Review E, vol.91, issue.4, p.42802, 2015. ,
DOI : 10.1103/PhysRevE.91.042802
Foundations of kinetic theory, Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, pp.171-197, 1954. ,
Statistical distributions of earthquake numbers: consequence of branching process, Geophysical Journal International, vol.180, issue.3, pp.1313-1328, 2010. ,
DOI : 10.1111/j.1365-246X.2009.04487.x
Poisson processes, volume 3 of Oxford Studies in Probability, 1993. ,
Probability theory : a comprehensive course, 2007. ,
Correlation-Based Analysis and Generation of Multiple Spike Trains Using Hawkes Models with an Exogenous Input, Frontiers in Computational Neuroscience, vol.4, 2010. ,
DOI : 10.3389/fncom.2010.00147
An example of Wold's Point Processes with Markov-Dependent Intervals, Journal of Applied Probability, vol.15, issue.4, pp.748-758, 1978. ,
Determinantal point process models and statistical inference, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.4, issue.4, pp.853-877, 2015. ,
DOI : 10.1111/rssb.12096
URL : https://hal.archives-ouvertes.fr/hal-01241077
Testing Statistical Hypotheses, 2005. ,
Simulation of nonhomogeneous poisson processes by thinning, Naval Research Logistics Quarterly, vol.32, issue.3, pp.403-413, 1979. ,
DOI : 10.1002/nav.3800260304
Multivariate hawkes processes, 2009. ,
Generation and Selection of Surrogate Methods for Correlation Analysis, Analysis of Parallel Spike Trains, pp.359-382, 2010. ,
DOI : 10.1007/978-1-4419-5675-0_17
Surrogate spike train generation through dithering in operational time, Frontiers in Computational Neuroscience, vol.4, 2010. ,
DOI : 10.3389/fncom.2010.00127
Mean field limit for disordered diffusions with singular interactions, The Annals of Applied Probability, vol.24, issue.5, pp.1946-1993, 2014. ,
DOI : 10.1214/13-AAP968
Transition from gaussian to non-gaussian fluctuations for mean-field diffusions in spatial interaction, 2015. ,
Stability results for a general class of interacting point processes dynamics, and applications. Stochastic processes and their applications, pp.1-30, 1998. ,
Population dynamics of interacting spiking neurons, Physical Review E, vol.66, issue.5, p.51917, 2002. ,
DOI : 10.1103/PhysRevE.66.051917
Geometry of sets and measures in Euclidean spaces : fractals and rectifiability. Number 44, 1999. ,
DOI : 10.1017/CBO9780511623813
Asymptotic behaviour of some interacting particle systems; McKean-Vlasov and Boltzmann models, Probabilistic models for nonlinear partial differential equations (Montecatini Terme, pp.42-95, 1995. ,
DOI : 10.1007/BF01055714
Rosenthal-type inequalities for the maximum of partial sums of stationary processes and examples. The Annals of Probability, pp.914-960, 2013. ,
Semimartingales : a course on stochastic processes, 1982. ,
DOI : 10.1515/9783110845563
Self-Exciting Point Process Modeling of Crime, Journal of the American Statistical Association, vol.106, issue.493, p.106, 2011. ,
DOI : 10.1198/jasa.2011.ap09546
Perfect simulation of hawkes processes, Advances in Applied Probability, pp.629-646, 2005. ,
Approximate Simulation of Hawkes Processes, Methodology and Computing in Applied Probability, vol.34, issue.2, pp.53-64, 2006. ,
DOI : 10.1007/s11009-006-7288-z
Statistical inference and simulation for spatial point processes, 2003. ,
DOI : 10.1201/9780203496930
Frequent item set mining for sequential data : Synchrony in neuronal spike trains. Intelligent Data Analysis, pp.997-1012, 2014. ,
Modelling trades-through in a limited order book using hawkes processes, Economics discussion paper, pp.2011-2043, 2011. ,
On Lewis' simulation method for point processes, IEEE Transactions on Information Theory, vol.27, issue.1, pp.23-30, 1981. ,
DOI : 10.1109/TIT.1981.1056305
Space-Time Point-Process Models for Earthquake Occurrences, Annals of the Institute of Statistical Mathematics, vol.50, issue.2, pp.379-402, 1998. ,
DOI : 10.1023/A:1003403601725
Analyse statistique des modèles de croissance-fragmentation, 2015. ,
On the simulation of large populations of neurons, Journal of Computational Neuroscience, vol.8, issue.1, pp.51-63, 2000. ,
DOI : 10.1023/A:1008964915724
Dynamics of a structured neuron population, Nonlinearity, vol.23, issue.1, p.55, 2010. ,
DOI : 10.1088/0951-7715/23/1/003
URL : https://hal.archives-ouvertes.fr/hal-00387413
Relaxation and Self-Sustained Oscillations in the Time Elapsed Neuron Network Model, SIAM Journal on Applied Mathematics, vol.73, issue.3, pp.1260-1279, 2013. ,
DOI : 10.1137/110847962
Adaptation and Fatigue Model for Neuron Networks and Large Time Asymptotics in a Nonlinear Fragmentation Equation, The Journal of Mathematical Neuroscience, vol.4, issue.1, pp.1-26, 2014. ,
DOI : 10.1016/j.jmaa.2005.12.036
URL : https://hal.archives-ouvertes.fr/hal-01054561
Cell assemblies as a guideline for brain research, Concepts in Neuroscience, vol.1, issue.1, pp.133-147, 1990. ,
Neuronal Spike Trains and Stochastic Point Processes, Biophysical Journal, vol.7, issue.4, pp.419-440, 1967. ,
DOI : 10.1016/S0006-3495(67)86597-4
URL : http://doi.org/10.1016/s0006-3495(67)86597-4
How Structure Determines Correlations in Neuronal Networks, PLoS Computational Biology, vol.52, issue.5, p.1002059, 2011. ,
DOI : 10.1371/journal.pcbi.1002059.s001
URL : http://doi.org/10.1371/journal.pcbi.1002059
Recurrent interactions in spiking networks with arbitrary topology, Physical Review E, vol.85, issue.3, p.31916, 2012. ,
DOI : 10.1103/PhysRevE.85.031916
URL : http://arxiv.org/abs/1201.0288
Transport equations in biology, 2006. ,
Activity in sparsely connected excitatory neural networks: effect of connectivity, Neural Networks, vol.11, issue.3, pp.415-434, 1998. ,
DOI : 10.1016/S0893-6080(97)00153-6
Spatio-temporal correlations and visual signalling in a complete neuronal population, Nature, vol.22, issue.7207, pp.454995-999, 2008. ,
DOI : 10.1038/nature07140
Significance of joint-spike events based on trial-shuffling by efficient combinatorial methods, Complexity, vol.52, issue.54, pp.79-86, 2003. ,
DOI : 10.1002/cplx.10085
Non-parametric significance estimation of joint-spike events by shuffling and resampling, Neurocomputing, vol.52, issue.54, pp.31-37, 2003. ,
DOI : 10.1016/S0925-2312(02)00823-8
Impact of Spike Train Autostructure on Probability Distribution of Joint Spike Events, Neural Computation, vol.5, issue.6, pp.1123-1163, 2013. ,
DOI : 10.1073/pnas.0809353105
Automatic spike train analysis and report generation. An implementation with R, R2HTML and STAR, Journal of Neuroscience Methods, vol.181, issue.1, pp.119-144, 2009. ,
DOI : 10.1016/j.jneumeth.2009.01.037
URL : https://hal.archives-ouvertes.fr/hal-00725386
A concise course on stochastic partial differential equations, 1905. ,
A microscopic spiking neuronal network for the age-structured model. arXiv preprint, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01121061
Detection and spatial characterization of minicolumnarity in the human cerebral cortex, Journal of Microscopy, vol.9, issue.Pt 4, pp.115-126, 2015. ,
DOI : 10.1111/jmi.12321
Central limit theorems for local martingales, Zeitschrift f???r Wahrscheinlichkeitstheorie und Verwandte Gebiete, vol.14, issue.3, pp.269-286, 1980. ,
DOI : 10.1007/BF00587353
Modeling and analyzing higher-order correlations in non-Poissonian spike trains, Journal of Neuroscience Methods, vol.208, issue.1, pp.18-33, 2012. ,
DOI : 10.1016/j.jneumeth.2012.04.015
Mean-Field Theory of Irregularly Spiking Neuronal Populations and Working Memory in Recurrent Cortical Networks, Computational Neuroscience : A comprehensive approach. Chapman & Hall/CRC Mathematical Biology and Medicine Series, 2004. ,
DOI : 10.1201/9780203494462.ch15
Continuous Martingales and Brownian Motion (Grundlehren der mathematischen Wissenschaften), 1999. ,
Goodness-of-Fit Tests and Nonparametric Adaptive Estimation for Spike Train Analysis, The Journal of Mathematical Neuroscience, vol.4, issue.1, pp.1-41, 2014. ,
DOI : 10.1109/TIT.1981.1056305
URL : https://hal.archives-ouvertes.fr/hal-00789127
Inference of functional connectivity in Neurosciences via Hawkes processes, 2013 IEEE Global Conference on Signal and Information Processing, 2013. ,
DOI : 10.1109/GlobalSIP.2013.6736879
URL : https://hal.archives-ouvertes.fr/hal-00866823
Some non asymptotic tail estimates for hawkes processes, Bulletin of the Belgian Mathematical Society-Simon Stevin, vol.13, issue.5, pp.883-896, 2007. ,
Adaptive estimation for hawkes processes ; application to genome analysis. The Annals of Statistics, pp.2781-2822, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00863958
Limit theorems for infinite-dimensional piecewise deterministic Markov processes. Applications to stochastic excitable membrane models, Electronic Journal of Probability, vol.17, issue.0, pp.171-219, 2012. ,
DOI : 10.1214/EJP.v17-1946
URL : http://arxiv.org/abs/1112.4069
Dynamical changes and temporal precision of synchronized spiking activity in monkey motor cortex during movement preparation, Journal of Physiology-Paris, vol.94, issue.5-6, pp.569-582, 2000. ,
DOI : 10.1016/S0928-4257(00)01100-1
Cancellation of a planned movement in monkey motor cortex, NeuroReport, vol.17, issue.3, pp.281-285, 2006. ,
DOI : 10.1097/01.wnr.0000201510.91867.a0
Time-frequency analysis of locally stationary hawkes processes. prepublication on HAL, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01502252
Synchrony: A Neural Correlate of Somatosensory Attention, Journal of Neurophysiology, vol.98, issue.3, pp.1645-1661, 2007. ,
DOI : 10.1152/jn.00522.2006
How do cell assemblies encode information in the brain?, Neuroscience & Biobehavioral Reviews, vol.23, issue.6, pp.785-796, 1999. ,
DOI : 10.1016/S0149-7634(99)00017-2
Théorie des distributions Publications de l'Institut de Mathématique de l'Université de Strasbourg, No. IX-X. Nouvelle édition, entiérement corrigée, refondue et augmentée, 1966. ,
Estimating the Firing Rate, Analysis of Parallel Spike Trains of Springer Series in Computational Neuroscience, pp.21-35, 2010. ,
DOI : 10.1007/978-1-4419-5675-0_2
Empirical processes with applications to statistics, Siam, vol.59, 2009. ,
DOI : 10.1137/1.9780898719017
Synchronization of Cortical Activity and its Putative Role in Information Processing and Learning, Annual Review of Physiology, vol.55, issue.1, pp.349-374, 1993. ,
DOI : 10.1146/annurev.ph.55.030193.002025
Dynamics of neural populations: Stability and synchrony, Network: Computation in Neural Systems, vol.66, issue.1, pp.3-29, 2006. ,
DOI : 10.1007/BF00335237
Modeling the Stochastic Gating of Ion Channels, Computational cell biology, pp.285-319, 2002. ,
DOI : 10.1007/978-0-387-22459-6_11
Kinetic equations from Hamiltonian dynamics: Markovian limits, Reviews of Modern Physics, vol.52, issue.3, p.569, 1980. ,
DOI : 10.1103/RevModPhys.52.569
Large scale dynamics of interacting particles, 2012. ,
DOI : 10.1007/978-3-642-84371-6
Unbiased estimation of precise temporal correlations between spike trains, Journal of Neuroscience Methods, vol.179, issue.1, pp.90-100, 2009. ,
DOI : 10.1016/j.jneumeth.2008.12.029
Équations de type de boltzmann, spatialement homogenes, Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete, pp.559-592, 1984. ,
DOI : 10.1007/bf00531891
Topics in propagation of chaos, Lecture Notes in Math, vol.22, issue.1, pp.165-251, 1991. ,
DOI : 10.1070/SM1974v022n01ABEH001689
Point process models with applications to safety and reliability, 2012. ,
DOI : 10.1007/978-1-4613-1067-9
Multiple Tests Based on a Gaussian Approximation of the Unitary Events Method with Delayed Coincidence Count, Neural Computation, vol.23, issue.8, p.7, 2014. ,
DOI : 10.1007/978-1-4419-5675-0_18
URL : https://hal.archives-ouvertes.fr/hal-00757323
Statistical analysis of temporal evolution in single-neuron firing rates, Biostatistics, vol.3, issue.1, pp.1-20, 2002. ,
DOI : 10.1093/biostatistics/3.1.1
Randomness in neurons : a multiscale probabilistic analysis, 2010. ,
Functional analysis, volume 123 of Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences, 1980. ,
Central Limit Theorem for Nonlinear Hawkes Processes, Journal of Applied Probability, vol.I, issue.03, pp.760-771, 2013. ,
DOI : 10.1214/aop/1065725193
URL : http://arxiv.org/abs/1204.1067
Nonlinear Hawkes Processes, 2013. ,