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Susanne Ditlevsen
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2020 – today
- 2024
- [j17]Jacob Stærk-Østergaard
, Anders Rahbek
High-Dimensional Cointegration and Kuramoto Inspired Systems. SIAM J. Appl. Dyn. Syst. 23(1): 236-255 (2024) - 2023
- [i1]Susanne Ditlevsen, Massimiliano Tamborrino, Irene Tubikanec:
Network inference in a stochastic multi-population neural mass model via approximate Bayesian computation. CoRR abs/2306.15787 (2023) - 2022
- [j16]Manh Cuong Ngô, Mads Peter Heide-Jørgensen, Susanne Ditlevsen:
Correction: Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence. PLoS Comput. Biol. 18(8) (2022) - 2021
- [j15]Manh Cuong Ngô
Detection of foraging behavior from accelerometer data using U-Net type convolutional networks. Ecol. Informatics 62: 101275 (2021) - 2020
- [j14]Susanne Ditlevsen
Transient dynamics of Pearson diffusions facilitates estimation of rate parameters. Commun. Nonlinear Sci. Numer. Simul. 82: 105034 (2020)
2010 – 2019
- 2019
- [j13]Manh Cuong Ngô
Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence. PLoS Comput. Biol. 15(3) (2019) - 2017
- [j12]Catalina Vich
Estimation of Synaptic Conductances in Presence of Nonlinear Effects Caused by Subthreshold Ionic Currents. Frontiers Comput. Neurosci. 11: 69 (2017) - [j11]Alexandre Iolov, Susanne Ditlevsen
Optimal Design for Estimation in Diffusion Processes from First Hitting Times. SIAM/ASA J. Uncertain. Quantification 5(1): 88-110 (2017) - 2016
- [j10]Kang Li, Vladislav Kozyrev, Søren Kyllingsbæk
Neurons in Primate Visual Cortex Alternate between Responses to Multiple Stimuli in Their Receptive Field. Frontiers Comput. Neurosci. 10: 141 (2016) - [j9]Henry C. Tuckwell, Susanne Ditlevsen
The Space-Clamped Hodgkin-Huxley System with Random Synaptic Input: Inhibition of Spiking by Weak Noise and Analysis with Moment Equations. Neural Comput. 28(10): 2129-2161 (2016) - 2015
- [j8]Marie Levakova
A review of the methods for neuronal response latency estimation. Biosyst. 136: 23-34 (2015) - 2014
- [j7]Marie Levakova
Estimating latency from inhibitory input. Biol. Cybern. 108(4): 475-493 (2014) - 2013
- [j6]Massimiliano Tamborrino
Parametric inference of neuronal response latency in presence of a background signal. Biosyst. 112(3): 249-257 (2013) - 2011
- [j5]Umberto Picchini
Practical estimation of high dimensional stochastic differential mixed-effects models. Comput. Stat. Data Anal. 55(3): 1426-1444 (2011) - [j4]Patrick Jahn, Rune W. Berg
Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process. J. Comput. Neurosci. 31(3): 563-579 (2011) - [j3]Susanne Ditlevsen
Firing Variability Is Higher than Deduced from the Empirical Coefficient of Variation. Neural Comput. 23(8): 1944-1966 (2011)
2000 – 2009
- 2008
- [j2]Petr Lánský
A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models. Biol. Cybern. 99(4-5): 253-262 (2008) - [j1]Umberto Picchini
Parameters of the Diffusion Leaky Integrate-and-Fire Neuronal Model for a Slowly Fluctuating Signal. Neural Comput. 20(11): 2696-2714 (2008)
Coauthor Index
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