Effects of transient subordinators on the firing statistics of a neuron model driven by dichotomous noise

2020 ◽  
Vol 102 (1) ◽  
Author(s):  
Romi Mankin ◽  
Astrid Rekker
Keyword(s):  
Neuron Model ◽  
Dichotomous Noise ◽  
Model Driven ◽  
Firing Statistics

scholarly journals Firing statistics of a neuron model driven by long-range correlated noise

2003 ◽  
Vol 68 (2) ◽  
Author(s):  
J. W. Middleton ◽  
M. J. Chacron ◽  
B. Lindner ◽  
A. Longtin
Keyword(s):  
Long Range ◽  
Neuron Model ◽  
Correlated Noise ◽  
Model Driven ◽  
Firing Statistics

Statistics of a neuron model driven by asymmetric colored noise

2015 ◽  
Vol 91 (2) ◽  
Author(s):  
Finn Müller-Hansen ◽  
Felix Droste ◽  
Benjamin Lindner
Keyword(s):  
Neuron Model ◽  
Colored Noise ◽  
Model Driven

Dynamical analysis of Josephson junction neuron model driven by a thermal signal and its digital implementation based on microcontroller

2021 ◽  
Vol 94 (12) ◽  
Author(s):  
Noel Freddy Fotie Foka ◽  
Balamurali Ramakrishnan ◽  
André Rodrigue Tchamda ◽  
Sifeu Takougang Kingni ◽  
Karthikeyan Rajagopal ◽  
...  
Keyword(s):  
Josephson Junction ◽  
Neuron Model ◽  
Dynamical Analysis ◽  
Digital Implementation ◽  
Model Driven ◽  
Thermal Signal

Dynamical response, information transition and energy dependence in a neuron model driven by autapse

2017 ◽  
Vol 90 (4) ◽  
pp. 2893-2902 ◽  
Author(s):  
Yuan Yue ◽  
Liwei Liu ◽  
Yujiang Liu ◽  
Yong Chen ◽  
Yueling Chen ◽  
...  
Keyword(s):  
Energy Dependence ◽  
Neuron Model ◽  
Dynamical Response ◽  
Model Driven ◽  
Response Information

scholarly journals Analytic Expressions for Rate and CV of a Type I Neuron Driven by White Gaussian Noise

2003 ◽  
Vol 15 (8) ◽  
pp. 1761-1788 ◽  
Author(s):  
Benjamin Lindner ◽  
André Longtin ◽  
Adi Bulsara
Keyword(s):  
White Noise ◽  
Neuron Model ◽  
First Passage Time ◽  
Input Parameter ◽  
Gaussian White Noise ◽  
Quadrature Formulas ◽  
Type I ◽  
Saddle Node ◽  
Type I Neuron ◽  
Firing Statistics

We study the one-dimensional normal form of a saddle-node system under the influence of additive gaussian white noise and a static “bias current” input parameter, a model that can be looked upon as the simplest version of a type I neuron with stochastic input. This is in contrast with the numerous studies devoted to the noise-driven leaky integrate-and-fire neuron. We focus on the firing rate and coefficient of variation (CV) of the interspike interval density, for which scaling relations with respect to the input parameter and noise intensity are derived. Quadrature formulas for rate and CV are numerically evaluated and compared to numerical simulations of the system and to various approximation formulas obtained in different limiting cases of the model. We also show that caution must be used to extend these results to the neuron model with multiplicative gaussian white noise. The correspondence between the first passage time statistics for the saddle-node model and the neuron model is obtained only in the Stratonovich interpretation of the stochastic neuron model, while previous results have focused only on the Ito interpretation. The correct Stratonovich interpretation yields CVs that are still relatively high, although smaller than in the Ito interpretation; it also produces certain qualitative differences, especially at larger noise intensities. Our analysis provides useful relations for assessing the distance to threshold and the level of synaptic noise in real type I neurons from their firing statistics. We also briefly discuss the effect of finite boundaries (finite values of threshold and reset) on the firing statistics.

Stratonovich model driven by dichotomous noise: analytical results

1988 ◽  
Vol 71 (3) ◽  
pp. 393-402 ◽  
Author(s):  
A. Teubel ◽  
U. Behn ◽  
A. K�hnel
Keyword(s):  
Dichotomous Noise ◽  
Model Driven ◽  
Analytical Results
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