Dynamics of time elapsed inhomogeneous neuron network model

Dynamics of elapsed time inhomogeneous neuron network model

Models for neural networks have been proposed which describe the probability to find a neuron for which time s has elapsed since the last discharge. These are written under the form of nonlinear age-structured equation where the total network activity modulates the firing rate. Here, we consider an inhomogeneous network of networks with variability on the refractory period. We give conditions o...

Relaxation and Self-Sustained Oscillations in the Time Elapsed Neuron Network Model

The time elapsed model describes the firing activity of an homogenous assembly of neurons thanks to the distribution of times elapsed since the last discharge. It gives a mathematical description of the probability density of neurons structured by this time. In an earlier work, based on generalized relative entropy methods, it is proved that for highly or weakly connected networks the model exh...

Intelligent identification of vehicle’s dynamics based on local model network

This paper proposes an intelligent approach for dynamic identification of the vehicles. The proposed approach is based on the data-driven identification and uses a high-performance local model network (LMN) for estimation of the vehicle’s longitudinal velocity, lateral acceleration and yaw rate. The proposed LMN requires no pre-defined standard vehicle model and uses measurement data to identif...

Dynamics of a discrete-time plant-herbivore model

Microscopic approach of a time elapsed neural model

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