scholarly journals Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival

2004 ◽  
Vol 98 (4-6) ◽  
pp. 442-451 ◽  
Author(s):  
Renaud Jolivet ◽  
Wulfram Gerstner
Keyword(s):  
Neuron Model ◽  
Model Driven

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

SYNCHRONIZATION AND DECODING INTERSPIKE INTERVALS

2002 ◽  
Vol 12 (05) ◽  
pp. 983-999 ◽  
Author(s):  
SEUNG KEE HAN ◽  
WON SUP KIM ◽  
HYUNGTAE KOOK
Keyword(s):  
Prediction Error ◽  
Neuron Model ◽  
Estimation Error ◽  
Temporal Coding ◽  
Reconstruction Method ◽  
Interspike Intervals ◽  
Model Driven ◽  
Attractor Reconstruction ◽  
Parameter Values ◽  
Deterministic Structures

Decoding of a sequence of interspike intervals (ISIs) of a neuron model driven by a chaotic stimulus is performed based on the attractor reconstruction method. As stimulus strength increases, both the stimulus estimation error and the prediction error in predicting stimulus crosswise by exploiting ISIs information tend to decrease with transitional drops at certain parameter values. It is analyzed that such behaviors are well explained in the context of synchronization between two chaotic patterns of stimulus and ISI sequence. The result implies that a new scheme of temporal coding at low firing rate regime can be achieved which exploits the preservation of nonlinear deterministic structures in stimulus.

Dynamical Response of Electrical Activities in Digital Neuron Circuit Driven by Autapse

2017 ◽  
Vol 27 (12) ◽  
pp. 1750187 ◽  
Author(s):  
Guodong Ren ◽  
Ping Zhou ◽  
Jun Ma ◽  
Ning Cai ◽  
Ahmed Alsaedi ◽  
...  
Keyword(s):  
Electromagnetic Induction ◽  
Numerical Scheme ◽  
Neuron Model ◽  
Biological Function ◽  
Digital Circuit ◽  
Dynamical Analysis ◽  
Dynamical Response ◽  
Neuron Models ◽  
Model Driven ◽  
Electrical Activities

Neuron models are available for computational neurodynamics and the main dynamical properties can be reproduced in the numerical scheme for further dynamical analysis. During model setting, some important biophysical factors should be considered and thus reliable neuron models can be approached. In this paper, a neuron model driven by autapse connection is investigated with the effect of electromagnetic induction being considered as well. A digital neuronal circuit is designed by using FPGA, the dynamical response and biological function of autapse connection. It is found that positive feedback in autapse can modulate the oscillating behaviors in the digital circuit, which could be effective for further investigation on digital neuronal network.

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