scholarly journals The Impact of Anomalous Diffusion on Action Potentials in Myelinated Neurons

2021 ◽  
Vol 5 (1) ◽  
pp. 4
Author(s):  
Corina S. Drapaca
Keyword(s):  
Fractional Order ◽  
Anomalous Diffusion ◽  
Action Potentials ◽  
Brain Diseases ◽  
Cable Equation ◽  
Nodes Of Ranvier ◽  
Temporal Derivative ◽  
Spatio Temporal ◽  
The Impact ◽  
Derivatives Of

Action potentials in myelinated neurons happen only at specialized locations of the axons known as the nodes of Ranvier. The shapes, timings, and propagation speeds of these action potentials are controlled by biochemical interactions among neurons, glial cells, and the extracellular space. The complexity of brain structure and processes suggests that anomalous diffusion could affect the propagation of action potentials. In this paper, a spatio-temporal fractional cable equation for action potentials propagation in myelinated neurons is proposed. The impact of the ionic anomalous diffusion on the distribution of the membrane potential is investigated using numerical simulations. The results show spatially narrower action potentials at the nodes of Ranvier when using spatial derivatives of the fractional order only and delayed or lack of action potentials when adding a temporal derivative of the fractional order. These findings could reveal the pathological patterns of brain diseases such as epilepsy, multiple sclerosis, and Alzheimer’s disease, which have become more prevalent in the latest years.

scholarly journals Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

2018 ◽  
Vol 8 (12) ◽  
pp. 2566 ◽  
Author(s):  
Jan Mucha ◽  
Jiri Mekyska ◽  
Zoltan Galaz ◽  
Marcos Faundez-Zanuy ◽  
Karmele Lopez-de-Ipina ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Fractional Order ◽  
Binary Classification ◽  
Archimedean Spiral ◽  
Discrimination Power ◽  
The Impact ◽  
The Relationship ◽  
Derivatives Of ◽  
Online Handwriting

Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessment.

The properties of Bessel-type fractional derivatives and integrals

2016 ◽  
pp. 3973-3982
Author(s):  
V. R. Lakshmi Gorty
Keyword(s):  
Fractional Order ◽  
Real Line ◽  
Computer Algebra System ◽  
Fractional Derivatives ◽  
Graphical Representation ◽  
Fractional Integrals ◽  
The Real ◽  
Fractional Derivatives And Integrals ◽  
Half Axis ◽  
Derivatives Of

The fractional integrals of Bessel-type Fractional Integrals from left-sided and right-sided integrals of fractional order is established on finite and infinite interval of the real-line, half axis and real axis. The Bessel-type fractional derivatives are also established. The properties of Fractional derivatives and integrals are studied. The fractional derivatives of Bessel-type of fractional order on finite of the real-line are studied by graphical representation. Results are direct output of the computer algebra system coded from MATLAB R2011b.

On Calculation Schemes of Spatial Gradient for Spatio-temporal Derivative Method

2003 ◽  
Vol 23 (Supplement1) ◽  
pp. 21-24
Author(s):  
Yasufumi YAMAMOTO ◽  
Yuya AKAMATSU ◽  
Noriyoshi YONEHARA ◽  
Tomomasa UEMURA
Keyword(s):  
Spatial Gradient ◽  
Derivative Method ◽  
Temporal Derivative ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 307
Author(s):  
Chi Zhang ◽  
Naixia Mou ◽  
Jiqiang Niu ◽  
Lingxian Zhang ◽  
Feng Liu
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Feedback Effect ◽  
Effect Of Temperature ◽  
The Tibetan Plateau ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
The Impact ◽  
Main Factor

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

scholarly journals Effects of memristive synapse radiation interactions on learning in spiking neural networks

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Sumedha Gandharava Dahl ◽  
Robert C. Ivans ◽  
Kurtis D. Cantley
Keyword(s):  
Action Potentials ◽  
Radiation Effects ◽  
Synaptic Weight ◽  
Learning Rule ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Synaptic Action ◽  
Drift Model ◽  
Spatio Temporal ◽  
Post Synaptic Neuron

AbstractThis study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-dependent plasticity (STDP) learning rule to recognize spatio-temporal patterns (STPs) representing 25 and 100-pixel characters. Memristive synapses based on a TiO2 non-linear drift model designed in Verilog-A are utilized, with STDP learning behavior achieved through bi-phasic pre- and post-synaptic action potentials. The models are modified to include experimentally observed state-altering and ionizing radiation effects on the device. It is found that radiation interactions tend to make the connection between afferents stronger by increasing the conductance of synapses overall, subsequently distorting the STDP learning curve. In the absence of consistent STPs, these effects accumulate over time and make the synaptic weight evolutions unstable. With STPs at lower flux intensities, the network can recover and relearn with constant training. However, higher flux can overwhelm the leaky integrate-and-fire post-synaptic neuron circuits and reduce stability of the network.

scholarly journals Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis
Keyword(s):  
Satellite Images ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Data Sources ◽  
Test Case ◽  
Temporal Data ◽  
Multiple Sources ◽  
Forecasting Error ◽  
Spatio Temporal ◽  
The Impact

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

scholarly journals Spatio-temporal dynamics of schistosomiasis in Rwanda between 2001 and 2012: impact of the national Neglected Tropical Disease control programme

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Elias Nyandwi ◽  
Tom Veldkamp ◽  
Frank Badu Osei ◽  
Sherif Amer
Keyword(s):  
Temporal Dynamics ◽  
Public Health Problem ◽  
Incidence Rates ◽  
Control Programme ◽  
Scan Statistics ◽  
Major Public Health Problem ◽  
Implementation Phase ◽  
Incidence Data ◽  
Spatio Temporal ◽  
The Impact

Schistosomiasis is recognised as a major public health problem in Rwanda. We aimed to identify the spatio-temporal dynamics of its distribution at a fine-scale spatial resolution and to explore the impact of control programme interventions. Incidence data of Schistosoma mansoni infection at 367 health facilities were obtained for the period 2001-2012. Disease cluster analyses were conducted using spatial scan statistics and geographic information systems. The impact of control interventions was assessed for three distinct sub-periods. Findings demonstrated persisting, emerging and re-emerging clusters of schistosomiasis infection across space and time. The control programme initially caused an abrupt increase in incidence rates during its implementation phase. However, this was followed by declining and disappearing clusters when the programme was fully in place. The findings presented should contribute to a better understanding of the dynamics of schistosomiasis distribution to be used when implementing future control activities, including prevention and elimination efforts.

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