Regular articleFiring Frequency of Leaky Intergrate-and-fire Neurons with Synaptic Current Dynamics
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Fractionally integrated Gauss-Markov processes and applications
2021, Communications in Nonlinear Science and Numerical SimulationDecision-time statistics of nonlinear diffusion models: Characterizing long sequences of subsequent trials
2020, Journal of Mathematical PsychologyCitation Excerpt :Real fluctuations, however, either distraction noise (stimulus components not belonging to the signal) or intrinsic noise, e.g. originating in the involved neural networks (Bair, Koch, Newsome, & Britten, 1994; Brunel, 2000; Dummer, Wieland, & Lindner, 2014; Pena, Vellmer, Bernardi, Roque, & Lindner, 2018), are always temporally correlated. One way to incorporate temporal correlations is a Markovian embedding which was developed first in statistical physics, see e.g. Guardia, Marchesoni, and Miguel (1984), Mori (1965) and Siegle, Goychuk, Talkner, and Hänggi (2010), but has also been frequently used in computational neuroscience for correlated Gaussian (Brunel & Sergi, 1998; Moreno, de la Rocha, Renart, & Parga, 2002; Moreno-Bote & Parga, 2010; Schwalger, Droste, & Lindner, 2015) and non-Gaussian noise (Droste & Lindner, 2014; Müller-Hansen, Droste, & Lindner, 2015). We have recently applied this method for integrate-and-fire neurons subject to an arbitrarily correlated Gaussian noise (Vellmer & Lindner, 2019).
Effects of Lévy noise in a neuronal competition model
2019, Physica A: Statistical Mechanics and its ApplicationsA multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics
2023, PLoS Computational Biology
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