scholarly journals Mathematical analysis of multiple spike time series synchronized in phase with collective theta wave using neural cell model

2020 ◽  
Author(s):  
Hiroki Noyama ◽  
Yuto Yoshikai ◽  
Kiyoshi Kotani ◽  
Yasuhiko Jimbo
Keyword(s):  
Time Series ◽  
Mathematical Analysis ◽  
Cell Model ◽  
Neural Cell ◽  
Spike Time ◽  
Theta Wave

Mathematical Analysis of Multiple Spike Time Series Synchronized in Phase with Collective Theta Wave Using Neural Cell Model

2020 ◽  
Vol 140 (9) ◽  
pp. 1050-1056
Author(s):  
Hiroki Noyama ◽  
Yuto Yoshikai ◽  
Kiyoshi Kotani ◽  
Yasuhiko Jimbo
Keyword(s):  
Time Series ◽  
Mathematical Analysis ◽  
Cell Model ◽  
Neural Cell ◽  
Spike Time ◽  
Theta Wave

A neural cell model of MSO

2002 ◽  
Author(s):  
Y. Han ◽  
S. Colburn
Keyword(s):  
Cell Model ◽  
Neural Cell

scholarly journals Time Series Forecasting Based Day-Ahead Energy Trading in Microgrids: Mathematical Analysis and Simulation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 63885-63900 ◽  
Author(s):  
Gyohun Jeong ◽  
Sangdon Park ◽  
Ganguk Hwang
Keyword(s):  
Time Series ◽  
Mathematical Analysis ◽  
Time Series Forecasting ◽  
Energy Trading

Regression derivatives and their application for registration of a tsunami wave arrival by sea-level records

2019 ◽  
pp. 28-41
Author(s):  
Сергей Мартикович Агаян ◽  
Шамиль Рафекович Богоутдинов ◽  
Ольга Васильевна Иванченко ◽  
Дмитрий Альфредович Камаев
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Sea Level ◽  
Discrete Time ◽  
Mathematical Analysis ◽  
Visual Analysis ◽  
Tsunami Wave ◽  
Information Support ◽  
Discrete Time Series ◽  
Wave Arrival

Структура дискретного временного ряда тесно связана со свойствами процесса, который он описывает. В рамках дискретного математического анализа имеется несколько подходов к анализу структуры дискретных рядов: геометрические меры, динамические коридоры и концепция тренда. Для дискретного временного ряда, заданного в общем случае на нерегулярной сетке, с характером тренда тесным образом связана регрессионная производная: области ее положительного (отрицательного) значения соответствуют возрастающим (убывающим) трендам, а границы между ними - экстремумам. В настоящей работе исследуются возможности применения методов дискретного математического анализа для разработки процедуры регистрации вступления волны цунами по оперативным данным измерения уровня моря. The research addresses the possibility of application of the methods of discrete mathematical analysis to develop a procedure for recording tsunami wave arrival on the base of the operational data for measuring sea level. As a basis for constructing a tsunami wave registration procedure, this research uses a schematization of the actions of the oceanographer on-duty during visual analysis of the sea level records. The task of automatic registration of a tsunami wave by sea level recording arises in various situations of information support of the oceanographer on duty. Requirements for the processing of sea level records depend on the situation. The structure of a discrete time series is closely related to the properties of the described process. As part of the discrete mathematical analysis, there are several approaches to the analysis of the structure of discrete series: geometric measures, dynamic corridors and the trend concept. For a discrete time series, given in the general case on an irregular grid, the regression derivative is closely related to the nature of the trend: the areas of its positive (negative) values correspond to the increasing (decreasing) trends, and the boundaries between them are extremes. The content of this research is a presentation of data processing techniques using regression derivatives, constructing data processing procedures based on derivatives, as well as a demonstration of their applicability to the problem of recording tsunami wave arrival according to the measuring of sea level.

scholarly journals Recovery of a sparse spike time series by L/sub 1/ norm deconvolution

1994 ◽  
Vol 42 (12) ◽  
pp. 3353-3365 ◽  
Author(s):  
M.S. O'Brien ◽  
A.N. Sinclair ◽  
S.M. Kramer
Keyword(s):  
Time Series ◽  
Spike Time

Smoothing of time series by the methods of Discrete Mathematical Analysis

2010 ◽  
Vol 11 (4) ◽  
pp. 1-5 ◽  
Author(s):  
S. M. Agayan ◽  
Sh. R. Bogoutdinov ◽  
A. D. Gvishiani ◽  
A. I. Kagan
Keyword(s):  
Time Series ◽  
Mathematical Analysis

Human electroencephalograms seen as fractal time series: Mathematical analysis and visualization

2006 ◽  
Vol 36 (3) ◽  
pp. 291-302 ◽  
Author(s):  
Vladimir Kulish ◽  
Alexei Sourin ◽  
Olga Sourina
Keyword(s):  
Time Series ◽  
Mathematical Analysis ◽  
Fractal Time

Mitochondrial dysfunction in a neural cell model of spinal muscular atrophy

2009 ◽  
Vol 87 (12) ◽  
pp. 2748-2756 ◽  
Author(s):  
Gyula Acsadi ◽  
Icksoo Lee ◽  
Xingli Li ◽  
Magomed Khaidakov ◽  
Alena Pecinova ◽  
...  
Keyword(s):  
Spinal Muscular Atrophy ◽  
Mitochondrial Dysfunction ◽  
Muscular Atrophy ◽  
Cell Model ◽  
Neural Cell
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