scholarly journals Feature Selection in Simple Neurons: How Coding Depends on Spiking Dynamics

2010 ◽  
Vol 22 (3) ◽  
pp. 581-598 ◽  
Author(s):  
Michael Famulare ◽  
Adrienne Fairhall
Keyword(s):  
Feature Selection ◽  
Information Transmission ◽  
Nonlinear Function ◽  
Sensory Systems ◽  
Statistical Characteristics ◽  
Neuron Models ◽  
Linear Filters ◽  
Feature Selectivity ◽  
Coding Strategy ◽  
The Relationship

The relationship between a neuron's complex inputs and its spiking output defines the neuron's coding strategy. This is frequently and effectively modeled phenomenologically by one or more linear filters that extract the components of the stimulus that are relevant for triggering spikes and a nonlinear function that relates stimulus to firing probability. In many sensory systems, these two components of the coding strategy are found to adapt to changes in the statistics of the inputs in such a way as to improve information transmission. Here, we show for two simple neuron models how feature selectivity as captured by the spike-triggered average depends on both the parameters of the model and the statistical characteristics of the input.

scholarly journals A Field Measurement Based Wind Characteristics Analysis of a Typhoon in Near-Ground Boundary Layer

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 873
Author(s):  
Dandan Xia ◽  
Liming Dai ◽  
Li Lin ◽  
Huaifeng Wang ◽  
Haitao Hu
Keyword(s):  
Turbulence Intensity ◽  
Field Measurement ◽  
Three Dimensional ◽  
Wind Data ◽  
Statistical Characteristics ◽  
Gust Factor ◽  
Characteristics Analysis ◽  
Wind Characteristics ◽  
The Mean ◽  
The Relationship

The field measurement was conducted to observe the wind field data of West Pacific typhoon “Maria” in this research. With the application of ultrasonic anemometers installed in different heights (10 m, 80 m, 100 m) of the tower, the three dimensional wind speed data of typhoon “Maria” was acquired. In addition, vane-type anemometers were installed to validate the accuracy of the wind data from ultrasonic anemometers. Wind characteristics such as the mean wind profile, turbulence intensity, integral length scale, and wind spectrum are studied in detail using the collected wind data. The relationship between the gust factor and turbulence intensity was also studied and compared with the existing literature to demonstrate the characteristics of Maria. The statistical characteristics of the turbulence intensity and gust factor are presented. The corresponding conclusion remarks are expected to provide a useful reference for designing wind-resistant buildings and structures.

scholarly journals Study on the Relationship between Electrical Tree Development and Partial Discharge of XLPE Cables

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Chaofei Gao ◽  
Yanlong Yu ◽  
Zan Wang ◽  
Wei Wang ◽  
Liwei Zheng ◽  
...  
Keyword(s):  
Statistical Characteristics ◽  
Time Frequency ◽  
Experimental Platform ◽  
Equivalent Time ◽  
Electrical Tree ◽  
Electrical Trees ◽  
Insulation Breakdown ◽  
The Relationship ◽  
Frequency Diagram ◽  
Tree Development

Based on the slice materials of 35 kV and 110 kV XLPE cables, an experimental platform is built to study the relationship between electrical tree and PDs in XLPE with different voltage levels. There are three significant statistical characteristics of the PDs during the growth of electrical trees. The analysis of the results shows that each growth stage has certain characteristics. Different features existed between the growth of the electrical trees and the PD in the insulation of the 35 and 110 kV cables. Evident characteristics such as large spans of time and frequency were present as the electrical trees grew violently in the equivalent time-frequency diagram at every stage. These results could provide criteria for the identification of the deterioration using PD to monitor cables in service at rated voltages. The results are important for the identification of defects in cable insulation in order to provide an early warning of insulation breakdown in the cables.

scholarly journals Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1816
Author(s):  
Hailun Xie ◽  
Li Zhang ◽  
Chee Peng Lim ◽  
Yonghong Yu ◽  
Han Liu
Keyword(s):  
Feature Selection ◽  
Particle Swarm ◽  
Nonlinear Function ◽  
Particle Swarm Optimisation ◽  
Data Sets ◽  
Discriminative Feature ◽  
Advanced Search ◽  
Selection For ◽  
Selection Tasks ◽  
New Strategies

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.

scholarly journals Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life

2020 ◽  
Vol 10 (9) ◽  
pp. 3282
Author(s):  
Angela Shin-Yu Lien ◽  
Yi-Der Jiang ◽  
Jia-Ling Tsai ◽  
Jawl-Shan Hwang ◽  
Wei-Chao Lin
Keyword(s):  
Quality Of Life ◽  
Feature Selection ◽  
Diabetic Nephropathy ◽  
Sleep Quality ◽  
Support Vector ◽  
Svm Classifier ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
The Relationship

Fatigue and poor sleep quality are the most common clinical complaints of people with diabetes mellitus (DM). These complaints are early signs of DM and are closely related to diabetic control and the presence of complications, which lead to a decline in the quality of life. Therefore, an accurate measurement of the relationship between fatigue, sleep status, and the complication of DM nephropathy could lead to a specific definition of fatigue and an appropriate medical treatment. This study recruited 307 people with Type 2 diabetes from two medical centers in Northern Taiwan through a questionnaire survey and a retrospective investigation of medical records. In an attempt to identify the related factors and accurately predict diabetic nephropathy, we applied hybrid research methods, integrated biostatistics, and feature selection methods in data mining and machine learning to compare and verify the results. Consequently, the results demonstrated that patients with diabetic nephropathy have a higher fatigue level and Charlson comorbidity index (CCI) score than without neuropathy, the presence of neuropathy leads to poor sleep quality, lower quality of life, and poor metabolism. Furthermore, by considering feature selection in selecting representative features or variables, we achieved consistence results with a support vector machine (SVM) classifier and merely ten representative factors and a prediction accuracy as high as 74% in predicting the presence of diabetic nephropathy.

Surrogate Population Models for Large-Scale Neural Simulations

2015 ◽  
Vol 27 (6) ◽  
pp. 1186-1222 ◽  
Author(s):  
Bryan P. Tripp
Keyword(s):  
Surrogate Model ◽  
Latent Variables ◽  
Large Scale ◽  
Neural Model ◽  
Surrogate Models ◽  
Weighted Sums ◽  
Neural Systems ◽  
Neuron Models ◽  
Linear Filters ◽  
Population Activity

Because different parts of the brain have rich interconnections, it is not possible to model small parts realistically in isolation. However, it is also impractical to simulate large neural systems in detail. This article outlines a new approach to multiscale modeling of neural systems that involves constructing efficient surrogate models of populations. Given a population of neuron models with correlated activity and with specific, nonrandom connections, a surrogate model is constructed in order to approximate the aggregate outputs of the population. The surrogate model requires less computation than the neural model, but it has a clear and specific relationship with the neural model. For example, approximate spike rasters for specific neurons can be derived from a simulation of the surrogate model. This article deals specifically with neural engineering framework (NEF) circuits of leaky-integrate-and-fire point neurons. Weighted sums of spikes are modeled by interpolating over latent variables in the population activity, and linear filters operate on gaussian random variables to approximate spike-related fluctuations. It is found that the surrogate models can often closely approximate network behavior with orders-of-magnitude reduction in computational demands, although there are certain systematic differences between the spiking and surrogate models. Since individual spikes are not modeled, some simulations can be performed with much longer steps sizes (e.g., 20 ms). Possible extensions to non-NEF networks and to more complex neuron models are discussed.

scholarly journals FOREST CERTIFICATION AS A TOOL TO ENSURE THE BALANCE OF FORESTRY LAND USE

2021 ◽  
Vol 71 (3) ◽  
Author(s):  
Stepan Dankevych
Keyword(s):  
Land Use ◽  
Regional Scale ◽  
Environmental Indicators ◽  
Statistical Characteristics ◽  
Forest Certification ◽  
Capital Investments ◽  
Forest Lands ◽  
The Impact ◽  
The Relationship ◽  
Over Time

The problem of ensuring the balanced use of forest lands determines the search for new economic and environmental tools that can influence this process. The need to improve the certification tool as part of the financial and economic mechanism for ensuring balanced forestry land use corresponds to the directions of state policy and European integration intentions of Ukraine, modern requirements of the ecological aspect of forestry land use. The work examines the practice in the field of forest certification in Ukraine from the point of view of balanced land use. Spatial-temporal analysis and assessment of the scale and dynamics of the spread of forest FSC certification in Ukraine has been carried out. The study was formed in three stages: (I) study of changes over time in the volume of forest certification on a national scale, (II) assessment of trends over time for indicators on a regional scale, (III) study of the relationship between individual indicators. The analysis of the impact of FSC-certification of forest management in Ukraine on the environmental indicators of forestry land use based on the results of the correlation between the statistical characteristics of certain economic and environmental indicators, such as the area of certified forests, capital investments, reforestation. Analysis of statistical data showed the relationship between environmental and economic performance over time and changes in specific characteristics on a regional scale. The study makes it possible, on the basis of an objectively existing causal relationship between phenomena and indicators, to identify the course of certain positive or negative processes in forestry land use. Forest certification can play a role in maintaining a balanced use of forest lands, preventing illegal logging, forest degradation and contributing to reforestation and capital investments. The study helps to identify certain key variables that limit the ability of forestry operators to ensure balanced use of forest lands and how forest certification can affect this. Foreign experience in stimulating forest certification has been investigated for the possibility of borrowing the experience of using management tools in order to motivate forest certification in Ukraine. It has been proven that certification is a significant environmental tool for ensuring a balanced level of land use and has the potential for further development.

scholarly journals An Examination of Relationship between Career Maturity and Multiple Factors by Feature Selection

2020 ◽  
Author(s):  
Shuxing Zhang ◽  
Qinneng Xu
Keyword(s):  
Feature Selection ◽  
Career Maturity ◽  
Selection Methods ◽  
School Students ◽  
Feasible Method ◽  
Multiple Factors ◽  
Sample Data ◽  
Forward Stepwise ◽  
The Relationship ◽  
Significant Factors

The purpose of this study is to investigate the relationship between career maturity and a branch of factors among senior school students. The sample data were collected from a total of 189 students. The linear relationship between career maturity and 72 factors were tested by using feature selection methods. LASSO and forward stepwise were compared based on crossvalidation. The results showed that LASSO was a feasible method to select the significant factors, and 12 of the total 72 factors were found to be important in predicting career maturity.

scholarly journals VESSEL CLASSIFICATION IN COSMO-SKYMED SAR DATA USING HIERARCHICAL FEATURE SELECTION

2015 ◽  
Vol XL-7/W3 ◽  
pp. 975-982 ◽  
Author(s):  
A. Makedonas ◽  
C. Theoharatos ◽  
V. Tsagaris ◽  
V. Anastasopoulos ◽  
S. Costicoglou
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Selection Procedure ◽  
Texture Features ◽  
Statistical Characteristics ◽  
Selection Scheme ◽  
Feature Extraction And Selection ◽  
Sar Data ◽  
Ship Classification ◽  
Analysis Scale

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features’ statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

Application of spectral analysis for determine the phase shift of signals with equal amplitudes using full-wave transformation when measuring the characteristics of weapons

2021 ◽  
pp. 12-23
Author(s):  
S. Tyshko ◽  
O. Lavrut ◽  
V. Smolar ◽  
O. Zabula ◽  
Yu. Chernichenko
Keyword(s):  
Spectral Analysis ◽  
Phase Shift ◽  
Information Transmission ◽  
Measurement Problem ◽  
Wave Transformation ◽  
Measuring Systems ◽  
Transmission Channels ◽  
Harmonic Signals ◽  
Full Wave ◽  
The Relationship

The article defines the list of technical characteristics of armaments and military equipment (ARM), the value of which is measured using phase methods. An analysis of known methods that have found wide application in measuring technology, which is designed to determine the technical characteristics associated with the measurement of phase shift during the development, manufacture and operation of weapons. Based on this analysis, it was determined that the measuring systems are designed to determine the phase shift of two harmonic signals in their composition have two channels of information transmission. This architecture of the implementation of measuring systems leads to the fact that a significant impact on the accuracy of the proposed measurement problem, makes a component of the error due to the phase symmetry of the signal transmission channels, as well as internal and external noise. As an alternative approach to solving the measurement problem of determining the phase shift of two harmonic signals, which will significantly reduce the error component due to phase asymmetry of information transmission channels, it is proposed to use the signal obtained by summing harmonic signals after full-wave transformation followed by spectral analysis. In order to implement the above approach, a measurement problem was set to determine the phase shift of two harmonic signals, using spectral analysis of the signal obtained by summing the harmonic signals after their full-wave transformation. A list of assumptions required for the synthesis of analytical relations that establish the relationship between the spectra of phases and amplitudes (power) of the signal obtained by summing harmonic signals after their full-wave transformation and phase shift of two harmonic signals. Analytical relationships are proposed that establish the relationship between the above characteristics. It is shown that the values of the spectrum of phases and amplitudes, which are calculated using the proposed expressions, differ from the values obtained in the calculations using the Fourier series coefficients, not more than 0.1%.

scholarly journals Correlated activity favors synergistic processing in local cortical networks in vitro at synaptically relevant timescales

2020 ◽  
Vol 4 (3) ◽  
pp. 678-697
Author(s):  
Samantha P. Sherrill ◽  
Nicholas M. Timme ◽  
John M. Beggs ◽  
Ehren L. Newman
Keyword(s):  
Information Processing ◽  
Mutual Information ◽  
Information Transmission ◽  
Cortical Circuits ◽  
Correlated Activity ◽  
Neural Information ◽  
Neural Information Processing ◽  
The Relationship ◽  
Information Values

Neural information processing is widely understood to depend on correlations in neuronal activity. However, whether correlation is favorable or not is contentious. Here, we sought to determine how correlated activity and information processing are related in cortical circuits. Using recordings of hundreds of spiking neurons in organotypic cultures of mouse neocortex, we asked whether mutual information between neurons that feed into a common third neuron increased synergistic information processing by the receiving neuron. We found that mutual information and synergistic processing were positively related at synaptic timescales (0.05–14 ms), where mutual information values were low. This effect was mediated by the increase in information transmission—of which synergistic processing is a component—that resulted as mutual information grew. However, at extrasynaptic windows (up to 3,000 ms), where mutual information values were high, the relationship between mutual information and synergistic processing became negative. In this regime, greater mutual information resulted in a disproportionate increase in redundancy relative to information transmission. These results indicate that the emergence of synergistic processing from correlated activity differs according to timescale and correlation regime. In a low-correlation regime, synergistic processing increases with greater correlation, and in a high-correlation regime, synergistic processing decreases with greater correlation.

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