Inference on the effect of non homogeneous inputs in Ornstein-Uhlenbeck neuronal modeling
From MaRDI portal
Publication:2045686
DOI10.3934/mbe.2020018zbMath1470.92047OpenAlexW2979588550WikidataQ91298278 ScholiaQ91298278MaRDI QIDQ2045686
Giuseppina Albano, Virginia Giorno
Publication date: 13 August 2021
Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3934/mbe.2020018
Related Items (4)
Inferring time non-homogeneous Ornstein Uhlenbeck type stochastic process ⋮ Detecting time-changes in \(PM_{10}\) during covid pandemic by means of an Ornstein Uhlenbeck type process ⋮ Time-inhomogeneous Feller-type diffusion process with absorbing boundary condition ⋮ Special issue: Advances in stochastic processes and applications
Cites Work
- Unnamed Item
- Unnamed Item
- Successive spike times predicted by a stochastic neuronal model with a variable input signal
- Nonparametric estimation in a mixed-effect Ornstein-Uhlenbeck model
- The parameters of the stochastic leaky integrate-and-fire neuronal model
- On the evaluation of firing densities for periodically driven neuron models
- Stochastic resonance in neuron models
- Gauss-Markov processes in the presence of a reflecting boundary and applications in neuronal models
- Restricted Ornstein-Uhlenbeck process and applications in neuronal models with periodic input signals
- A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties
- Estimation of the input parameters in the Feller neuronal model
- Handbook of Monte Carlo Methods
- Stochastic Differential Mixed-Effects Models
- Parameters of the Diffusion Leaky Integrate-and-Fire Neuronal Model for a Slowly Fluctuating Signal
- Introduction to Theoretical Neurobiology
- Estimation in Discretely Observed Diffusions Killed at a Threshold
- How Sample Paths of Leaky Integrate-and-Fire Models Are Influenced by the Presence of a Firing Threshold
This page was built for publication: Inference on the effect of non homogeneous inputs in Ornstein-Uhlenbeck neuronal modeling
