A Novel Method for the Optimal Parameter Selection of Discrete-Time Switch Model

The paper proposes a novel method for the optimal parameter selection of the discrete-time switch model used in circuit solvers that adopt the Fixed Admittance Matrix Nodal Method (FAMNM) approach. As known, FAMNM-based circuit solvers allow to reach efficient computation times since they do not need the inversion of the circuit nodal admittance matrix. However, these solvers need to optimally tune the so-called discrete switch conductance, since this parameter might largely affect the simulations accuracy. Within this context, the method proposed in the paper minimizes the distance between the eigenvalues of the original circuit’s nodal admittance matrix with those associated with the presence of the discrete-time switches. The method is proven to provide values of the discrete-time switch conductance that maximize the simulation accuracy and minimize the losses on this artificial parameter. The performances of the proposed method are finally validated by making reference to two test cases: (i) a circuit composed of RLC elements, (ii) a network model that includes a single-phase transmission line.

A Method for the Assessment of the Optimal Parameter of Discrete-Time Switch Model

Chrysanthi Foti, Mario Paolone, Reza Razzaghi

This paper proposes a novel method for the optimal parameter selection of the discrete-time switch model used in circuit solvers that adopt the fixed admittance matrix nodal method (FAMNM) approach. As known, FAMNM-based circuit solvers allow to reach efficient computation times, in particular for real-time simulation applications, since they do not need the inversion of the circuit nodal admittance matrix. However, these solvers need to optimally tune the so-called discrete switch conductance, since this parameter might largely affect the simulations accuracy. Within this context, we propose a method for the determination of the discrete-time switch conductance which is obtained by minimizing the distance between the eigenvalues of the original circuit's nodal admittance matrix with those associated with the circuit including the discrete-time switches. The method is proven to provide values of the discrete-time switch conductance that maximize the simulation accuracy and minimize the losses on this artificially introduced parameter. Additionally, the proposed method avoids the use of trial-and-error process typically required when discrete-time switch conductances need to be addressed in FAMNM approach. The performances of the proposed method are demonstrated for circuits with single and multiple switches in which passive RLC elements and transmission lines are both considered.

Elsevier2014

Simple Neuron Models and Adaptation

Skander Mensi

Predicting activity of single neuron is an important part of the computational neuroscience and a great challenge. Several mathematical models exist, from the simple (one compartment and few parameters, like the SRM or the IF-type models), to the more complex (biophysical model like the Hodgkin-Huxley model). All these models have their own advantages and limitations, but no one is able to reproduce the exact behavior of real neurons. Multiple projects try to simulate complex neural networks, or even the whole brain (i.e. the blue brain project or other big network simulation). To achieve this goal it is very important that simple neuron models simulate, for a low computational cost, the precise activities of all neuron classes. It is well-known that neurons exhibit a lot of different activity patterns (from an electro-physiological point of view). Here we have focused on pyramidal neurons from the layer 5 of the neocortex. They are classified as regular spiking-cells. This cell type shows adaptation and like other neurons, refractoriness. Adaptation and refractoriness are very common neuronal activity in the brain, and so it is important to have a simple model which can reproduce this kind of activity. This project deals with two classical simple neuron models: the adaptive exponential integrate-and-fire model (AdEx) and the spike response model (SRM). We deter- mined the parameters of these two models using data generated with a detailed model, the Destexhe's model which is a HH-like model for cortical pyramidal cells, stimulated with different current injection scenarios. In a second time the model parameters have been set using data from in-vitro recordings of 4 layer 5 pyramidal rat neurons, stimulated with a sinusoid in vivo-like protocol, injected somatically in current-clamp configuration. Then we show that this type of model can capture adaptation and can reproduce the activity of neuron with a high reliability

2008

Simple Neuron Models and Adaptation

Skander Mensi

2008