On a set of data for the membrane potential in a neuron.
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[1] Susanne Ditlevsen,et al. Estimation of the input parameters in the Ornstein-Uhlenbeck neuronal model. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.
[2] M. Hofmann. Lp estimation of the diffusion coefficient , 1999 .
[3] F. Comte,et al. Adaptive estimation of mean and volatility functions in (auto-)regressive models , 2002 .
[4] Marc Hoffmann. ON ESTIMATING THE DIFFUSION COEFFICIENT: PARAMETRIC VERSUS NONPARAMETRIC , 2001 .
[5] Henry C. Tuckwell,et al. Stochastic processes in the neurosciences , 1989 .
[6] T. Alderweireld,et al. A Theory for the Term Structure of Interest Rates , 2004, cond-mat/0405293.
[7] P. Lánský,et al. Diffusion approximation of the neuronal model with synaptic reversal potentials , 1987, Biological Cybernetics.
[8] N. Shephard,et al. LIMIT THEOREMS FOR BIPOWER VARIATION IN FINANCIAL ECONOMETRICS , 2005, Econometric Theory.
[9] P Lánský,et al. The stochastic diffusion models of nerve membrane depolarization and interspike interval generation. , 1999, Journal of the peripheral nervous system : JPNS.
[10] Yutaka Sakai,et al. The Ornstein-Uhlenbeck Process Does Not Reproduce Spiking Statistics of Neurons in Prefrontal Cortex , 1999, Neural Computation.
[11] Laura Sacerdote,et al. On the comparison of Feller and Ornstein-Uhlenbeck models for neural activity , 1995, Biological Cybernetics.
[12] R. Capocelli,et al. Diffusion approximation and first passage time problem for a model neuron , 1971, Biological cybernetics.
[13] A. Tsybakov,et al. Introduction à l'estimation non-paramétrique , 2003 .
[14] Jean Jacod,et al. A Central Limit Theorem for Realised Power and Bipower Variations of Continuous Semimartingales , 2004 .
[15] D. Florens-zmirou. On estimating the diffusion coefficient from discrete observations , 1993, Journal of Applied Probability.
[16] Yuri Kabanov,et al. From Stochastic Calculus to Mathematical Finance. The Shiryaev Festschrift , 2006 .
[17] Susanne Ditlevsen,et al. Estimation of the input parameters in the Feller neuronal model. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.
[18] Non‐parametric Estimation of the Death Rate in Branching Diffusions , 2002 .
[19] Peter C. Kiessler,et al. Statistical Inference for Ergodic Diffusion Processes , 2006 .
[20] Reinhard Höpfner,et al. A stochastic model and a functional central limit theorem for information processing in large systems of neurons , 2006, Journal of mathematical biology.
[21] Henry C. Tuckwell,et al. The response of a spatially distributed neuron to white noise current injection , 1979, Biological Cybernetics.
[22] P. Lánský,et al. Diffusion approximation and first-passage-time problem for a model neuron III. Abirth-and-death process approach , 1988 .
[23] Laura Sacerdote,et al. The Ornstein–Uhlenbeck neuronal model with signal-dependent noise , 2001 .
[24] Non‐parametric Kernel Estimation of the Coefficient of a Diffusion , 2000 .