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 …
Stochastic resonance in continuous and spiking neuron models with Levy noise
A Patel, B Kosko - IEEE Transactions on Neural Networks, 2008 - ieeexplore.ieee.org
Levy noise can help neurons detect faint or subthreshold signals. Levy noise extends
standard Brownian noise to many types of impulsive jump-noise processes found in real and …
standard Brownian noise to many types of impulsive jump-noise processes found in real and …
[HTML][HTML] Disentangling the stochastic behavior of complex time series
Complex systems involving a large number of degrees of freedom, generally exhibit non-
stationary dynamics, which can result in either continuous or discontinuous sample paths of …
stationary dynamics, which can result in either continuous or discontinuous sample paths of …
Data-driven reconstruction of stochastic dynamical equations based on statistical moments
Stochastic processes are encountered in many contexts, ranging from generation sizes of
bacterial colonies and service times in a queueing system to displacements of Brownian …
bacterial colonies and service times in a queueing system to displacements of Brownian …
jumpdiff: A Python library for statistical inference of jump-diffusion processes in observational or experimental data sets
We introduce a Python library, called jumpdiff, which includes all necessary functions to
assess jump-diffusion processes. This library includes functions which compute a set of non …
assess jump-diffusion processes. This library includes functions which compute a set of non …
[HTML][HTML] 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 …
Reinforcement Learning for Jump-Diffusions
We study continuous-time reinforcement learning (RL) for stochastic control in which system
dynamics are governed by jump-diffusion processes. We formulate an entropy-regularized …
dynamics are governed by jump-diffusion processes. We formulate an entropy-regularized …
Physics-informed and data-driven discovery of governing equations for complex phenomena in heterogeneous media
M Sahimi - Physical Review E, 2024 - APS
Rapid evolution of sensor technology, advances in instrumentation, and progress in
devising data-acquisition software and hardware are providing vast amounts of data for …
devising data-acquisition software and hardware are providing vast amounts of data for …
Exact simulation of jump-diffusion processes with Monte Carlo applications
B Casella, GO Roberts - Methodology and Computing in Applied …, 2011 - Springer
We introduce a novel algorithm (JEA) to simulate exactly from a class of one-dimensional
jump-diffusion processes with state-dependent intensity. The simulation of the continuous …
jump-diffusion processes with state-dependent intensity. The simulation of the continuous …
Analysis and data-driven reconstruction of bivariate jump-diffusion processes
We introduce the bivariate jump-diffusion process, consisting of two-dimensional diffusion
and two-dimensional jumps, that can be coupled to one another. We present a data-driven …
and two-dimensional jumps, that can be coupled to one another. We present a data-driven …