Stochastic Biomathematical Models with Applications to Neuronal Modeling, 2013. ,
Balanced Inhibition and Excitation Drive Spike Activity in Spinal Half-Centers, Science, vol.315, issue.5810, pp.315-390, 2007. ,
DOI : 10.1126/science.1134960
Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations, Journal of Neurophysiology, vol.110, issue.4, pp.1021-1034, 2013. ,
DOI : 10.1152/jn.00006.2013
Estimation in Discretely Observed Diffusions Killed at a Threshold, Scandinavian Journal of Statistics, vol.73, issue.2, pp.274-293, 2013. ,
DOI : 10.1093/biomet/73.3.573
Estimating input parameters from intracellular recordings in the Feller neuronal model, Physical Review E, vol.2, issue.3, p.31916, 2010. ,
DOI : 10.1080/03610919908813596
A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input, Biological Cybernetics, vol.68, issue.1, pp.1-19, 2006. ,
DOI : 10.1007/978-3-642-93059-1
Inference in Hidden Markov Models, 2005. ,
Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons, Neuroscience, vol.107, issue.1, pp.13-24, 2001. ,
DOI : 10.1016/S0306-4522(01)00344-X
URL : https://hal.archives-ouvertes.fr/hal-00124691
A novel method for characterizing synaptic noise in cortical neurons, Neurocomputing, vol.58, issue.60, pp.58-60, 2004. ,
DOI : 10.1016/j.neucom.2004.01.042
URL : https://hal.archives-ouvertes.fr/hal-00018646
Stochastic Biomathematical Models with Applications to Neuronal Modeling, chapter Introduction to Stochastic Models in Biology, 2013. ,
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
The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes, Scandinavian Journal of Statistics, vol.1, issue.3, pp.438-465, 2008. ,
DOI : 10.21314/JCF.2001.089
Random Walk Models for the Spike Activity of a Single Neuron, Biophysical Journal, vol.4, issue.1, pp.41-68, 1964. ,
DOI : 10.1016/S0006-3495(64)86768-0
Spiking Neuron Models, 2002. ,
Neuronal Dynamics . From single neurons to networks and models of cognition, 2014. ,
Inference for stochastic neuronal models, Applied Mathematics and Computation, vol.38, issue.1, pp.51-73, 1990. ,
DOI : 10.1016/0096-3003(90)90080-M
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
On a set of data for the membrane potential in a neuron, Mathematical Biosciences, vol.207, issue.2, pp.275-301, 2007. ,
DOI : 10.1016/j.mbs.2006.10.009
Smoothing of, and Parameter Estimation from, Noisy Biophysical Recordings, PLoS Computational Biology, vol.87, issue.60, 2009. ,
DOI : 10.1371/journal.pcbi.1000379.s001
Efficient Estimation of Detailed Single-Neuron Models, Journal of Neurophysiology, vol.96, issue.2, pp.872-890, 2006. ,
DOI : 10.1152/jn.00079.2006
Simulation and Inference for Stochastic Differential Equations with R examples, 2008. ,
Dynamical Systems in Neuroscience, 2007. ,
Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process, Journal of Computational Neuroscience, vol.21, issue.11, pp.563-579, 2011. ,
DOI : 10.1162/neco.2009.06-08-807
Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model, Physical Review E, vol.117, issue.4, p.41114, 2012. ,
DOI : 10.1016/j.spa.2012.04.006
Numerical solution of stochastic differential equations, 1992. ,
Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods, Biological Cybernetics, vol.105, issue.3, pp.155-167, 2012. ,
DOI : 10.1007/s00422-011-0459-1
Stochastic Methods in Neuroscience, 2010. ,
DOI : 10.1093/acprof:oso/9780199235070.001.0001
Input parameters in a one-dimensional neuronal model with reversal potentials, Biosystems, vol.48, issue.1-3, pp.123-129, 1998. ,
DOI : 10.1016/S0303-2647(98)00078-1
Inference for the diffusion models of neuronal activity, Mathematical Biosciences, vol.67, issue.2, pp.247-260, 1983. ,
DOI : 10.1016/0025-5564(83)90103-7
The parameters of the stochastic leaky integrate-and-fire neuronal model, Journal of Computational Neuroscience, vol.21, issue.2, pp.211-223, 2006. ,
DOI : 10.1007/s10827-006-8527-6
Stochastic Differential Equations: An Introduction with Applications, 2010. ,
Comparing integrate-and-fire models estimated using intracellular and extracellular data, Neurocomputing, vol.65, issue.66, pp.379-385, 2005. ,
DOI : 10.1016/j.neucom.2004.10.032
A new look at state-space models for neural data, Iss. SI), pp.107-126, 2010. ,
DOI : 10.1137/1.9781611970128
Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods, Journal of Computational Neuroscience, vol.27, issue.35, pp.1-19, 2012. ,
DOI : 10.1523/JNEUROSCI.2865-07.2007
Data Augmentation for Diffusions, Journal of Computational and Graphical Statistics, vol.85, issue.3, pp.665-688, 2013. ,
DOI : 10.1093/biomet/85.1.240
Parameters of the Diffusion Leaky Integrate-and-Fire Neuronal Model for a Slowly Fluctuating Signal, Neural Computation, vol.75, issue.2, pp.20-2696, 2008. ,
DOI : 10.1523/JNEUROSCI.4897-03.2004
Calculating Event-Triggered Average Synaptic Conductances From the Membrane Potential, Journal of Neurophysiology, vol.97, issue.3, pp.2544-2552, 2007. ,
DOI : 10.1152/jn.01000.2006
URL : https://hal.archives-ouvertes.fr/hal-00120638
Characterizing neuronal activity by describing the membrane potential as a stochastic process, Journal of Physiology-Paris, vol.103, issue.1-2, pp.98-106, 2009. ,
DOI : 10.1016/j.jphysparis.2009.05.010
URL : https://hal.archives-ouvertes.fr/hal-00444900
Extracting synaptic conductances from single membrane potential traces, Neuroscience, vol.158, issue.2, pp.545-52, 2009. ,
DOI : 10.1016/j.neuroscience.2008.10.033
URL : https://hal.archives-ouvertes.fr/hal-00377094
Statistical inference for diffusion type processes, 1999. ,
On inference for partially observed nonlinear diffusion models using the Metropolis-Hastings algorithm, Biometrika, vol.88, issue.3, pp.603-621, 2001. ,
DOI : 10.1093/biomet/88.3.603
Characterization of Subthreshold Voltage Fluctuations in Neuronal Membranes, Neural Computation, vol.85, issue.11, pp.2577-2618, 2003. ,
DOI : 10.1006/jcph.1996.5638
URL : https://hal.archives-ouvertes.fr/hal-00123773
A Method to Estimate Synaptic Conductances From Membrane Potential Fluctuations, Journal of Neurophysiology, vol.91, issue.6, pp.91-2884, 2004. ,
DOI : 10.1152/jn.01223.2003
URL : https://hal.archives-ouvertes.fr/hal-00018654
Estimation of synaptic conductances and their variances from intracellular recordings of neocortical neurons in vivo, Neurocomputing, vol.58, issue.60, pp.387-392, 2004. ,
DOI : 10.1016/j.neucom.2004.01.071
URL : https://hal.archives-ouvertes.fr/hal-00018652
A contrast estimator for completely or partially observed hypoelliptic diffusion, Stochastic Processes and their Applications, pp.2521-2552, 2012. ,
DOI : 10.1016/j.spa.2012.04.006
URL : https://hal.archives-ouvertes.fr/hal-00714352
Parametric Inference for Diffusion Processes Observed at Discrete Points in Time: a Survey, International Statistical Review, vol.3, issue.3, pp.337-354, 2004. ,
DOI : 10.1017/S0266466600012044
Statistical Methods for Stochastic Differential Equations, chapter Estimating functions for diffusion type processes, 2012. ,
Introduction to theoretical neurobiology Nonlinear and stochastic theories, 1988. ,