scholarly journals Experimentally constrained CA1 fast-firing parvalbumin-positive interneuron network models exhibit sharp transitions into coherent high frequency rhythms

2013 ◽  
Vol 7 ◽  
Author(s):  
Katie A. Ferguson ◽  
Carey Y. L. Huh ◽  
Bénédicte Amilhon ◽  
Sylvain Williams ◽  
Frances K. Skinner
Keyword(s):  
High Frequency ◽  
Network Models ◽  
Fast Firing

Artificial Higher Order Neural Network Models

2016 ◽  
pp. 241-311
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
High Frequency ◽  
Learning Algorithm ◽  
Network Models ◽  
Higher Order ◽  
Neural Network Models ◽  
Ultra High Frequency

This chapter introduces the background of HONN model developing history and overview 24 applied artificial higher order neural network models. This chapter provides 24 HONN models and uses a single uniform HONN architecture for ALL 24 HONN models. This chapter also uses a uniform learning algorithm for all 24 HONN models and uses a uniform weight update formulae for all 24 HONN models. In this chapter, Polynomial HONN, Trigonometric HONN, Sigmoid HONN, SINC HONN, and Ultra High Frequency HONN structure and models are overviewed too.

Artificial Higher Order Neural Network Models

2016 ◽  
pp. 282-350
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
High Frequency ◽  
Learning Algorithm ◽  
Network Models ◽  
Higher Order ◽  
Neural Network Models ◽  
Ultra High Frequency

This chapter introduces the background of HONN model developing history and overview 24 applied artificial higher order neural network models. This chapter provides 24 HONN models and uses a single uniform HONN architecture for ALL 24 HONN models. This chapter also uses a uniform learning algorithm for all 24 HONN models and uses a uniform weight update formulae for all 24 HONN models. In this chapter, Polynomial HONN, Trigonometric HONN, Sigmoid HONN, SINC HONN, and Ultra High Frequency HONN structure and models are overviewed too.

STATISTICAL AND MACHINE LEARNING HIGH-FREQUENCY TIME SERIES FORECASTING METHODS IN AUTOMATIC MODE

2020 ◽  
pp. 3-11
Author(s):  
Sai Van Cuong ◽  
M. V. Shcherbakov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
High Frequency ◽  
Network Models ◽  
Time Series Forecasting ◽  
Neural Network Models ◽  
Automatic Mode ◽  
Forecasting Methods ◽  
The Neural Network

The research of the problem of automatic high-frequency time series forecasting (without expert) is devoted. The efficiency of high-frequency time series forecasting using different statistical and machine learning modelsis investigated. Theclassical statistical forecasting methods are compared with neural network models based on 1000 synthetic sets of high-frequency data. The neural network models give better prediction results, however, it takes more time to compute compared to statistical approaches.

Rainfall Estimation Using Neuron-Adaptive Higher Order Neural Networks

2021 ◽  
pp. 375-414
Keyword(s):  
Neural Network ◽  
High Frequency ◽  
Network Models ◽  
Higher Order ◽  
Rainfall Estimation ◽  
Neural Network Models ◽  
Real World Data ◽  
Higher Order Neural Networks ◽  
Optimum Model ◽  
Discontinuous Data

Real-world data is often nonlinear, discontinuous, and may comprise high frequency, multi-polynomial components. Not surprisingly, it is hard to find the best models for modeling such data. Classical neural network models are unable to automatically determine the optimum model and appropriate order for data approximation. In order to solve this problem, neuron-adaptive higher order neural network (NAHONN) models have been introduced. Definitions of one-dimensional, two-dimensional, and n-dimensional NAHONN models are studied. Specialized NAHONN models are also described. NAHONN models are shown to be “open box.” These models are further shown to be capable of automatically finding not only the optimum model but also the appropriate order for high frequency, multi-polynomial, discontinuous data. Rainfall estimation experimental results confirm model convergence. The authors further demonstrate that NAHONN models are capable of modeling satellite data.

Models of Artificial Higher Order Neural Networks

2021 ◽  
pp. 1-96
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Frequency ◽  
Learning Algorithm ◽  
Network Models ◽  
Higher Order ◽  
Neural Network Models ◽  
Ultra High Frequency ◽  
Higher Order Neural Networks

This chapter introduces the background of the higher order neural network (HONN) model developing history and overviews 24 applied artificial higher order neural network models. This chapter provides 24 HONN models and uses a single uniform HONN architecture for all 24 HONN models. This chapter also uses a uniform learning algorithm for all 24 HONN models and uses uniform weight update formulae for all 24 HONN models. In this chapter, polynomial HONN, Trigonometric HONN, Sigmoid HONN, SINC HONN, and Ultra High Frequency HONN structure and models are overviewed too.

Modeling and Quantification of Acoustic Damping Induced by Vortex Shedding in Non-Compact Thermoacoustic Systems

2019 ◽  
Author(s):  
Thomas Hofmeister ◽  
Tobias Hummel ◽  
Bruno Schuermans ◽  
Thomas Sattelmayer
Keyword(s):  
Vortex Shedding ◽  
High Frequency ◽  
Low Frequency ◽  
Network Models ◽  
Test Case ◽  
Acoustic Damping ◽  
Linearized Euler Equations ◽  
Damping Rate ◽  
Frequency Regime ◽  
Swirl Stabilized Combustion

Abstract This paper presents a methodology to compute acoustic damping rates of transversal, high-frequency modes induced by vortex-shedding. The acoustic damping rate presents one key quantity for the assessment of the linear thermoacoustic stability of gas turbine combustors. State of the art network models — as employed to calculate damping rates in low-frequency, longitudinal systems — cannot fulfill this task due to the acoustic non-compactness encountered in the high-frequency regime. Furthermore, it is yet unclear, whether direct eigensolutions of the Linearized Euler Equations (LEE), which capture the mechanism of vortex shedding, yield correct damping rate results constituted by the implicit presence of acoustic as well as hydrodynamic contributions in these solutions. The methodology’s applicability to technically relevant systems is demonstrated by a validation test case using a lab-scale, swirl-stabilized combustion system.

scholarly journals Shared Signature Dynamics Tempered by Local Fluctuations Enables Fold Adaptability and Specificity

2019 ◽  
Vol 36 (9) ◽  
pp. 2053-2068 ◽  
Author(s):  
She Zhang ◽  
Hongchun Li ◽  
James M Krieger ◽  
Ivet Bahar
Keyword(s):  
High Frequency ◽  
Family Members ◽  
Network Models ◽  
Intermediate Frequency ◽  
Functional Differentiation ◽  
Global Dynamics ◽  
Structural Variations ◽  
Mode Spectrum ◽  
Large Ensembles ◽  
Premise Structure

AbstractRecent studies have drawn attention to the evolution of protein dynamics, in addition to sequence and structure, based on the premise structure-encodes-dynamics-encodes-function. Of interest is to understand how functional differentiation is accomplished while maintaining the fold, or how intrinsic dynamics plays out in the evolution of structural variations and functional specificity. We performed a systematic computational analysis of 26,899 proteins belonging to 116 CATH superfamilies. Characterizing cooperative mechanisms and convergent/divergent features that underlie the shared/differentiated dynamics of family members required a methodology that lends itself to efficient analyses of large ensembles of proteins. We therefore introduced, SignDy, an integrated pipeline for evaluating the signature dynamics of families based on elastic network models. Our analysis confirmed that family members share conserved, highly cooperative (global) modes of motion. Importantly, our analysis discloses a subset of motions that sharply distinguishes subfamilies, which lie in a low-to-intermediate frequency regime of the mode spectrum. This regime has maximal impact on functional differentiation of families into subfamilies, while being evolutionarily conserved among subfamily members. Notably, the high-frequency end of the spectrum also reveals evolutionary conserved features across and within subfamilies; but in sharp contrast to global motions, high-frequency modes are minimally collective. Modulation of robust/conserved global dynamics by low-to-intermediate frequency fluctuations thus emerges as a versatile mechanism ensuring the adaptability of selected folds and the specificity of their subfamilies. SignDy further allows for dynamics-based categorization as a new layer of information relevant to distinctive mechanisms of action of subfamilies, beyond sequence or structural classifications.

The Fabrication and Dielectric Constant of (1-3) Piezoceramic Polymer Composites

2016 ◽  
Vol 690 ◽  
pp. 39-44 ◽  
Author(s):  
Jerapong Tontrakoon ◽  
Gobwute Rujijanagul ◽  
Kamonpan Pengpat ◽  
Sukum Eitssayeam ◽  
Uraiwan Intatha ◽  
...  
Keyword(s):  
Dielectric Constant ◽  
Polymer Composites ◽  
High Frequency ◽  
Sintering Temperature ◽  
Firing Process ◽  
Glass Forming ◽  
Scale Size ◽  
Capacitance Model ◽  
Scanning Electron ◽  
Fast Firing

Piezoceramic-polymer composites having (1-3) type connectivity and of a scale size suitable for high frequency >1 MHz transducers was carried out in this study. The piezoceramics (PbZr0.52Ti0.48O3, PZT) were prepared by the conventional mixed oxide route. The starting powders of PbO, ZrO2 and TiO2 were mixed and calcined at 800oC. The calcined powder was mixed with excess PbO and a lithium/bismuth-based glass forming in order to lower the sintering temperature to approximately 1000oC. A method for extruding rods of approximately 400 m diameter was developed. The fast firing process was carried out to sinter the PZT rods. Then the rods were assembled and impregnated with epoxy resin to form 1-3 composites containing approximately 20 and 50 vol% piezoceramics. Both PZT rods and the composites were studied by a scanning electron microscope (SEM). The dielectric properties of the composites were measured. The equivalent capacitance model was employed to determine the dielectric for comparison.

Modeling and Quantification of Acoustic Damping Induced by Vortex Shedding in Noncompact Thermoacoustic Systems

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Thomas Hofmeister ◽  
Tobias Hummel ◽  
Bruno Schuermans ◽  
Thomas Sattelmayer
Keyword(s):  
Vortex Shedding ◽  
High Frequency ◽  
Low Frequency ◽  
Network Models ◽  
Test Case ◽  
Acoustic Damping ◽  
Linearized Euler Equations ◽  
Damping Rate ◽  
Frequency Regime ◽  
Swirl Stabilized Combustion

Abstract This paper presents a methodology to compute acoustic damping rates of transversal, high-frequency modes induced by vortex-shedding. The acoustic damping rate presents one key quantity for the assessment of the linear thermoacoustic stability of gas turbine combustors. State-of-the-art network models—as employed to calculate damping rates in low-frequency, longitudinal systems—cannot fulfill this task due to the acoustic noncompactness encountered in the high-frequency regime. Furthermore, it is yet unclear, whether direct eigensolutions of the linearized Euler equations (LEE), which capture the mechanism of vortex shedding, yield correct damping rate results constituted by the implicit presence of acoustic as well as hydrodynamic contributions in these solutions. The methodology's applicability to technically relevant systems is demonstrated by a validation test case using a lab-scale, swirl-stabilized combustion system.

What's in a model? Network models as tools instead of representations of what psychiatric disorders really are

2019 ◽  
Vol 42 ◽  
Author(s):  
Hanna M. van Loo ◽  
Jan-Willem Romeijn
Keyword(s):  
Psychiatric Disorders ◽  
Network Models ◽  
Model Network

AbstractNetwork models block reductionism about psychiatric disorders only if models are interpreted in a realist manner – that is, taken to represent “what psychiatric disorders really are.” A flexible and more instrumentalist view of models is needed to improve our understanding of the heterogeneity and multifactorial character of psychiatric disorders.

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