Stochastic integrate and fire models: a review on mathematical methods and their applications
L Sacerdote, MT Giraudo - … models: with applications to neuronal modeling, 2013 - Springer
Mathematical models are an important tool for neuroscientists. During the last 30 years
many papers have appeared on single neuron description and specifically on stochastic …
many papers have appeared on single neuron description and specifically on stochastic …
A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models
P Lansky, S Ditlevsen - Biological cybernetics, 2008 - Springer
Parameters in diffusion neuronal models are divided into two groups; intrinsic and input
parameters. Intrinsic parameters are related to the properties of the neuronal membrane and …
parameters. Intrinsic parameters are related to the properties of the neuronal membrane and …
Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold
Information is transmitted in the brain through various kinds of neurons that respond
differently to the same signal. Full characteristics including cognitive functions of the brain …
differently to the same signal. Full characteristics including cognitive functions of the brain …
A benchmark test for a quantitative assessment of simple neuron models
Several methods and algorithms have recently been proposed that allow for the systematic
evaluation of simple neuron models from intracellular or extracellular recordings. Models …
evaluation of simple neuron models from intracellular or extracellular recordings. Models …
The quantitative single-neuron modeling competition
As large-scale, detailed network modeling projects are flourishing in the field of
computational neuroscience, it is more and more important to design single neuron models …
computational neuroscience, it is more and more important to design single neuron models …
Stochastic differential mixed‐effects models
U Picchini, ADE GAETANO… - Scandinavian Journal of …, 2010 - Wiley Online Library
Stochastic differential equations have been shown useful in describing random continuous
time processes. Biomedical experiments often imply repeated measurements on a series of …
time processes. Biomedical experiments often imply repeated measurements on a series of …
Comparative study of different integrate-and-fire neurons: Spontaneous activity, dynamical response, and stimulus-induced correlation
Stochastic integrate-and-fire (IF) neuron models have found widespread applications in
computational neuroscience. Here we present results on the white-noise-driven perfect …
computational neuroscience. Here we present results on the white-noise-driven perfect …
Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process
P Jahn, RW Berg, J Hounsgaard… - Journal of computational …, 2011 - Springer
Stochastic leaky integrate-and-fire models are popular due to their simplicity and statistical
tractability. They have been widely applied to gain understanding of the underlying …
tractability. They have been widely applied to gain understanding of the underlying …
[图书][B] Introduction to statistical data analysis for the life sciences
CT Ekstrom, H Sørensen - 2014 - books.google.com
A Hands-On Approach to Teaching Introductory Statistics Expanded with over 100 more
pages, Introduction to Statistical Data Analysis for the Life Sciences, Second Edition …
pages, Introduction to Statistical Data Analysis for the Life Sciences, Second Edition …
[HTML][HTML] Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms
S Wiqvist, A Golightly, AT McLean, U Picchini - Computational Statistics & …, 2021 - Elsevier
Stochastic differential equation mixed-effects models (SDEMEMs) are flexible hierarchical
models that are able to account for random variability inherent in the underlying time …
models that are able to account for random variability inherent in the underlying time …