scholarly journals An Iterative Calculation Method of Neuron Model with Sigmoid Function

2002 ◽  
Vol 122 (3) ◽  
pp. 381-389
Author(s):  
Naoya CHUJO ◽  
Susumu KUROYANAG ◽  
Shinji DOKI ◽  
Shigeru OKUMA
Keyword(s):  
Calculation Method ◽  
Neuron Model ◽  
Sigmoid Function ◽  
Iterative Calculation

A Power Efficient Hardware Implementation of the IF Neuron Model

2018 ◽  
pp. 140-154
Author(s):  
Shuquan Wang ◽  
Shasha Guo ◽  
Lei Wang ◽  
Nan Li ◽  
Zikai Nie ◽  
...  
Keyword(s):  
Neuron Model ◽  
Hardware Implementation ◽  
Power Efficient

scholarly journals Smooth Nonlinear Fitting Scheme for Analog Multiplierless Implementation of Hindmarsh-Rose Neuron Model

2021 ◽  
Author(s):  
Jianming Cai ◽  
Han Bao ◽  
Quan Xu ◽  
Zhongyun Hua ◽  
Bocheng Bao
Keyword(s):  
Large Scale ◽  
Neuron Model ◽  
Hardware Implementation ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Implementation Cost ◽  
Hyperbolic Tangent ◽  
Nonlinear Fitting ◽  
Chaotic Bursting ◽  
Electrical Activities

Abstract The Hindmarsh-Rose (HR) neuron model is built to describe the neuron electrical activities. Due to the polynomial nonlinearities, multipliers are required to implement the HR neuron model in analog. In order to avoid the multipliers, this brief presents a novel smooth nonlinear fitting scheme. We first construct two nonlinear fitting functions using the composite hyperbolic tangent functions and then implement an analog multiplierless circuit for the two-dimensional (2D) or three- dimensional (3D) HR neuron model. To exhibit the nonlinear fitting effects, numerical simulations and hardware experiments for the fitted HR neuron model are provided successively. The results show that the fitted HR neuron model with analog multiplierless circuit can display different operation patterns of resting, periodic spiking, and periodic/chaotic bursting, entirely behaving like the original HR neuron model. The analog multiplierless circuit has the advantage of low implementation cost and thereby it might be suitable for the hardware implementation of large-scale neural networks.

Definition of geodetic height namely by measured geocentric coordinates

2017 ◽  
Vol 921 (3) ◽  
pp. 20-23
Author(s):  
Y.P. Kureniov ◽  
T.N. Malik
Keyword(s):  
Calculation Method ◽  
Moving Target ◽  
Iterative Calculation ◽  
Geodetic Height ◽  
Definition Of ◽  
Geocentric Coordinates

The article describes one of the methods for determining the geodetic height by using the satellite as a moving target points. It is shown that the chronology of the development of the satellite method for determining the geodetic height of the iterative calculation method for the open-closed formulas for the dependence of the geodetic latitude and, finally, to closed formulas determining the geodetic height in function exclusively from geocentric coordinates. This article describes the geometrical (volumetric and flat) models to perform the derivation of the formulas for determining the geodetic height as a function of the geocentric coordinates of the point. Two variants of the formulas obtained by the authors to determine the geodetic height.

Web-enabled Neuron Model Hardware Implementation and Testing

2015 ◽  
Author(s):  
Fearghal Morgan ◽  
Finn Krewer ◽  
Frank Callaly ◽  
Aedan Coffey ◽  
Brian Mc Ginley
Keyword(s):  
Neuron Model ◽  
Hardware Implementation
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