scholarly journals Characterization of Neuronal Dynamics at Rest by Fractal Dimension

2021 ◽  
Vol 168 ◽  
pp. S37-S38
Author(s):  
Camillo Porcaro
Keyword(s):  
Fractal Dimension ◽  
Neuronal Dynamics

scholarly journals Electrophysiological and Transcriptomic Features Reveal a Circular Taxonomy of Cortical Neurons

2021 ◽  
Vol 15 ◽  
Author(s):  
Alejandro Rodríguez-Collado ◽  
Cristina Rueda
Keyword(s):  
Cortical Neurons ◽  
Cell Types ◽  
Mammalian Brain ◽  
Neuronal Dynamics ◽  
Electrophysiological Signals ◽  
Visual Cortex Neurons ◽  
Line Level ◽  
Mouse Visual Cortex ◽  
Different Levels

The complete understanding of the mammalian brain requires exact knowledge of the function of each neuron subpopulation composing its parts. To achieve this goal, an exhaustive, precise, reproducible, and robust neuronal taxonomy should be defined. In this paper, a new circular taxonomy based on transcriptomic features and novel electrophysiological features is proposed. The approach is validated by analysing more than 1850 electrophysiological signals of different mouse visual cortex neurons proceeding from the Allen Cell Types database. The study is conducted on two different levels: neurons and their cell-type aggregation into Cre lines. At the neuronal level, electrophysiological features have been extracted with a promising model that has already proved its worth in neuronal dynamics. At the Cre line level, electrophysiological and transcriptomic features are joined on cell types with available genetic information. A taxonomy with a circular order is revealed by a simple transformation of the first two principal components that allow the characterization of the different Cre lines. Moreover, the proposed methodology locates other Cre lines in the taxonomy that do not have transcriptomic features available. Finally, the taxonomy is validated by Machine Learning methods which are able to discriminate the different neuron types with the proposed electrophysiological features.

scholarly journals Electrophysiological and Transcriptomic Features Reveal a Circular Taxonomy of Cortical Neurons

2021 ◽  
Author(s):  
Alejandro Rodríguez-Collado ◽  
Cristina Rueda
Keyword(s):  
Cortical Neurons ◽  
Cell Types ◽  
Mammalian Brain ◽  
Neuronal Dynamics ◽  
Electrophysiological Signals ◽  
Visual Cortex Neurons ◽  
Line Level ◽  
Mouse Visual Cortex ◽  
Different Levels

The complete understanding of the mammalian brain requires exact knowledge of the function of each of the neurons composing its parts. To achieve this goal, an exhaustive, precise, reproducible, and robust neuronal taxonomy should be defined. In this paper, a new circular taxonomy based on transcriptomic features and novel electrophysiological features is proposed. The approach is validated by analysing more than 1850 electrophysiological signals of different mouse visual cortex neurons proceeding from the Allen Cell Types Database. The study is conducted on two different levels: neurons and their cell-type aggregation into Cre Lines. At the neuronal level, electrophysiological features have been extracted with a promising model that has already proved its worth in neuronal dynamics. At the Cre Line level, electrophysiological and transcriptomic features are joined on cell types with available genetic information. A taxonomy with a circular order is revealed by a simple transformation of the first two principal components that allow the characterization of the different Cre Lines. Moreover, the proposed methodology locates other Cre Lines in the taxonomy that do not have transcriptomic features available. Finally, the taxonomy is validated by Machine Learning methods which are able to discriminate the different neuron types with the proposed electrophysiological features.

Soft Computing Based Statistical Time Series Analysis, Characterization of Chaos Theory, and Theory of Fractals

2013 ◽  
pp. 30-61 ◽  
Author(s):  
Mofazzal H. Khondekar ◽  
Dipendra N. Ghosh ◽  
Koushik Ghosh ◽  
Anup Kumar Bhattacharya
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Time Series Analysis ◽  
Soft Computing ◽  
Chaos Theory ◽  
Filter Design ◽  
Theoretical Perspective ◽  
Series Analysis ◽  
Statistical Time

The present work is an attempt to analyze the various researches already carried out from the theoretical perspective in the field of soft computing based time series analysis, characterization of chaos, and theory of fractals. Emphasis has been given in the analysis on soft computing based study in prediction, data compression, explanatory analysis, signal processing, filter design, tracing chaotic behaviour, and estimation of fractal dimension of time series. The present work is a study as a whole revealing the effectiveness as well as the shortcomings of the various techniques adapted in this regard.

Characterization of Melanophore Morphology by Fractal Dimension Analysis

2004 ◽  
Vol 17 (2) ◽  
pp. 165-172 ◽  
Author(s):  
Victoria A. Kimler ◽  
Mary Tracy-Bee ◽  
Candace D. Ollie ◽  
Renee M. Langer ◽  
James M. Montante ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Dimension Analysis ◽  
Fractal Dimension Analysis

FRACTAL CHARACTERIZATION OF DYNAMIC FRACTURE NETWORK EXTENSION IN POROUS MEDIA

Fractals ◽  
2017 ◽  
Vol 25 (02) ◽  
pp. 1750023 ◽  
Author(s):  
JIANCHAO CAI ◽  
WEI WEI ◽  
XIANGYUN HU ◽  
RICHENG LIU ◽  
JINJIE WANG
Keyword(s):  
Porous Media ◽  
Fractal Dimension ◽  
Moisture Content ◽  
Oil And Gas ◽  
Engineering Application ◽  
Fracture Network ◽  
Drying Time ◽  
Form Complex ◽  
Network Extension

Fracture network and fractured porous media as well as their transport properties have received great attentions in many fields from engineering application and basic theoretical researches. Fracture will dynamically extend in length and aperture to form complex fracture network under some external conditions such as percussion drilling, wave propagation, desiccation and hydrofracturing. The complexity of fracture network can be well quantitatively characterized by fractal dimension. In this work, the dynamic characterization of fracture network extension in porous media under drying process is measured by the improved box-counting technique, and fractal dimensions of fracture network are respectively related to drying time, average aperture, moisture content and fracture porosity. The fractal dimension increases exponentially with drying time and average aperture, and decreases with moisture content in the form of power law. Specially, the fractal dimension is approximatively increased with porosity in the form of linearity in a narrow porosity range. The transport capacity of fracture network, described by seepage coefficient, is also related to the fractal dimension with drying time in the form of exponential function. The presented fractal analysis of fracture network could also shed light on the hydrofracturing application in subsurface unconventional oil and gas reservoirs.

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