The parameters of the stochastic leaky integrate-and-fire neuronal model
P Lansky, P Sanda, J He - Journal of Computational Neuroscience, 2006 - Springer
Five parameters of one of the most common neuronal models, the diffusion leaky integrate-
and-fire model, also known as the Ornstein-Uhlenbeck neuronal model, were estimated on …
and-fire model, also known as the Ornstein-Uhlenbeck neuronal model, were estimated on …
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 …
Parameter estimation for a leaky integrate-and-fire neuronal model from ISI data
P Mullowney, S Iyengar - Journal of Computational Neuroscience, 2008 - Springer
Abstract The Ornstein-Uhlenbeck process has been proposed as a model for the
spontaneous activity of a neuron. In this model, the firing of the neuron corresponds to the …
spontaneous activity of a neuron. In this model, the firing of the neuron corresponds to the …
On a stochastic leaky integrate-and-fire neuronal model
A Buonocore, L Caputo, E Pirozzi, LM Ricciardi - Neural computation, 2010 - direct.mit.edu
The leaky integrate-and-fire neuronal model proposed in Stevens and Zador, in which time
constant and resting potential are postulated to be time dependent, is revisited within a …
constant and resting potential are postulated to be time dependent, is revisited within a …
The Gamma renewal process as an output of the diffusion leaky integrate-and-fire neuronal model
P Lansky, L Sacerdote, C Zucca - Biological cybernetics, 2016 - Springer
Statistical properties of spike trains as well as other neurophysiological data suggest a
number of mathematical models of neurons. These models range from entirely descriptive …
number of mathematical models of neurons. These models range from entirely descriptive …
Parameters of the diffusion leaky integrate-and-fire neuronal model for a slowly fluctuating signal
Stochastic leaky integrate-and-fire (LIF) neuronal models are common theoretical tools for
studying properties of real neuronal systems. Experimental data of frequently sampled …
studying properties of real neuronal systems. Experimental data of frequently sampled …
The effect of a random initial value in neural first-passage-time models
P Lánský, CE Smith - Mathematical biosciences, 1989 - Elsevier
The effect of a random initial value is examined in several stochastic integrate-and-fire
neural models with a constant threshold and a constant input. The three models considered …
neural models with a constant threshold and a constant input. The three models considered …
On the return process with refractoriness for a non-homogeneous Ornstein-Uhlenbeck neuronal model
An Ornstein-Uhlenbeck diffusion process is considered as a model for the membrane
potential activity of a single neuron. We assume that the neuron is subject to a sequence of …
potential activity of a single neuron. We assume that the neuron is subject to a sequence of …
Estimating parameters of generalized integrate-and-fire neurons from the maximum likelihood of spike trains
Y Dong, S Mihalas, A Russell… - Neural …, 2011 - ieeexplore.ieee.org
When a neuronal spike train is observed, what can we deduce from it about the properties of
the neuron that generated it? A natural way to answer this question is to make an …
the neuron that generated it? A natural way to answer this question is to make an …
A first-passage-time analysis of the periodically forced noisy leaky integrate-and-fire model
T Shimokawa, K Pakdaman, T Takahata, S Tanabe… - Biological …, 2000 - Springer
We present a general method for the analysis of the discharge trains of periodically forced
noisy leaky integrate-and-fire neuron models. This approach relies on the iterations of a …
noisy leaky integrate-and-fire neuron models. This approach relies on the iterations of a …