scholarly journals Quantification and classification of neuronal responses in kernel-smoothed peristimulus time histograms

2015 ◽  
Vol 113 (4) ◽  
pp. 1260-1274 ◽  
Author(s):  
Michael R. H. Hill ◽  
Itzhak Fried ◽  
Christof Koch
Keyword(s):  
Random Noise ◽  
Large Data ◽  
Neuronal Responses ◽  
Data Set ◽  
Firing Rates ◽  
Kernel Convolution ◽  
Actual Response ◽  
Response Envelope ◽  
Convolution Method

Peristimulus time histograms are a widespread form of visualizing neuronal responses. Kernel convolution methods transform these histograms into a smooth, continuous probability density function. This provides an improved estimate of a neuron's actual response envelope. We here develop a classifier, called the h-coefficient, to determine whether time-locked fluctuations in the firing rate of a neuron should be classified as a response or as random noise. Unlike previous approaches, the h-coefficient takes advantage of the more precise response envelope estimation provided by the kernel convolution method. The h-coefficient quantizes the smoothed response envelope and calculates the probability of a response of a given shape to occur by chance. We tested the efficacy of the h-coefficient in a large data set of Monte Carlo simulated smoothed peristimulus time histograms with varying response amplitudes, response durations, trial numbers, and baseline firing rates. Across all these conditions, the h-coefficient significantly outperformed more classical classifiers, with a mean false alarm rate of 0.004 and a mean hit rate of 0.494. We also tested the h-coefficient's performance in a set of neuronal responses recorded in humans. The algorithm behind the h-coefficient provides various opportunities for further adaptation and the flexibility to target specific parameters in a given data set. Our findings confirm that the h-coefficient can provide a conservative and powerful tool for the analysis of peristimulus time histograms with great potential for future development.

Massive Data Classification of Neural Responses

2010 ◽  
pp. 278-298
Author(s):  
Pedro Tomás ◽  
IST TU Lisbon ◽  
Aleksandar Ilic ◽  
Leonel Sousa
Keyword(s):  
Execution Time ◽  
Data Parallelism ◽  
Data Sets ◽  
Neural Responses ◽  
Neuronal Responses ◽  
Data Set ◽  
Web Interfaces ◽  
Mass Classification ◽  
Neuronal Code

When analyzing the neuronal code, neuroscientists usually perform extra-cellular recordings of neuronal responses (spikes). Since the size of the microelectrodes used to perform these recordings is much larger than the size of the cells, responses from multiple neurons are recorded by each micro-electrode. Thus, the obtained response must be classified and evaluated, in order to identify how many neurons were recorded, and to assess which neuron generated each spike. A platform for the mass-classification of neuronal responses is proposed in this chapter, employing data-parallelism for speeding up the classification of neuronal responses. The platform is built in a modular way, supporting multiple web-interfaces, different back-end environments for parallel computing or different algorithms for spike classification. Experimental results on the proposed platform show that even for an unbalanced data set of neuronal responses the execution time was reduced of about 45%. For balanced data sets, the platform may achieve a reduction in execution time equal to the inverse of the number of back-end computational elements.

scholarly journals Prediction of Misclassification Data using Cognitive Bayes Computation Techniques (COBACO)

2020 ◽  
Vol 9 (3) ◽  
pp. 928-932
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Large Data ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Accuracy Rate ◽  
Data Set ◽  
Predictive Values ◽  
Time Operation

Missing data arise major issues in the large database regarding quantitative analysis. Due to this issues, the inference of the computational process produce bias results, more damage of data, the error rate can increase, and more difficult to accomplish the process of imputation. Prediction of disguised missing data occurs in the large data sets are another major problems in real time operation. Machine learning (ML) techniques to connect with the classification of measurement to enforce the accuracy rate of predictive values. These techniques overcome the various challenges to the problem of losing data. Recent work based on the prediction of misclassification using supervised ML approach; to predict an output for an unseen input with limited parameters in a data set. When increase the size of parameter, then it generates the outcome of less accuracy rate. This article presented a new approach COBACO, an effective supervised machine learning technique. Several strategies describe the classification of predictive techniques for missing data analysis in efficient supervised machine learning techniques. The proposed predictive techniques COBACO generated more precise, accurate results than the other predictive approaches. The Experimental results obtained using both real and synthetic data set show that the proposed approach offers a valuable and promising insight to the problem of prediction of missing information.

scholarly journals Choosing alternatives: Using Bayesian Networks and memory-based learning to study the dative alternation

2013 ◽  
Vol 9 (2) ◽  
pp. 227-262 ◽  
Author(s):  
Daphne Theijssen ◽  
Louis ten Bosch ◽  
Lou Boves ◽  
Bert Cranen ◽  
Hans van Halteren
Keyword(s):  
Logistic Regression ◽  
Bayesian Networks ◽  
Large Data ◽  
Semantic Features ◽  
Data Set ◽  
Logistic Regression Models ◽  
Dative Alternation ◽  
Lexical Items ◽  
Actual Classification

AbstractIn existing research on syntactic alternations such as the dative alternation, (give her the apple vs. give the apple to her), the linguistic data is often analysed with the help of logistic regression models. In this article, we evaluate the use of logistic regression for this type of research, and present two different approaches: Bayesian Networks and Memory-based learning. For the Bayesian Network, we use the higher-level semantic features suggested in the literature, while we limit ourselves to lexical items in the memory-based approach. We evaluate the suitability of the three approaches by applying them to a large data set (>11,000 instances) extracted from the British National Corpus, and comparing their quality in terms of classification accuracy, their interpretability in the context of linguistic research, and their actual classification of individual cases. Our main finding is that the classifications are very similar across the three approaches, also when employing lexical items instead of the higher-level features, because most of the alternation is determined by the verb and the length of the two objects (here: her and the apple).

Identifying Bands in the Knowledge Exchange Spectrum in an Online Health Infomediary

2015 ◽  
Vol 10 (3) ◽  
pp. 63-84 ◽  
Author(s):  
Dobin Yim ◽  
Jiban Khuntia ◽  
Young Argyris
Keyword(s):  
Cluster Analysis ◽  
Cosmetic Surgery ◽  
Knowledge Exchange ◽  
Large Data ◽  
Two Dimensions ◽  
Data Set ◽  
Large Data Set ◽  
Similarities And Differences

Online health infomediaries have the objective of knowledge exchange between participants. Visitor contribution is an important factor for the success of the infomediaries. Providers engaged with infomediaries need visitor identification for reputational incentives. However, identification or classification of visitors in online health infomediaries is sparse in literature. This study proposes two dimensions of participation, the intention and intensity levels of visitors, to conceptualize four user categories: community supporters, experiencer providers, knowledge questors, and expertise contributors. The authors validate these categories using a unique large data set collected from a health infomediary for cosmetic surgery, and consisting of 162,598 observed activities of 44,350 visitors, at different participation levels in the year 2012-13. They use cluster analysis to describe similarities and differences among the four user categories. Practice implications are discussed.

Formal definitions of Slovakian mire plant associations and their application in regional research

Biologia ◽  
2007 ◽  
Vol 62 (4) ◽  
Author(s):  
Daniel Dítě ◽  
Michal Hájek ◽  
Petra Hájková
Keyword(s):  
Large Data ◽  
Vegetation Types ◽  
Regional Research ◽  
Water Conductivity ◽  
Data Set ◽  
Study Region ◽  
Calcium Gradient ◽  
Plant Associations ◽  
Formal Definitions

AbstractWe applied the Cocktail method to a large data set of 4 117 relevés of all Slovak vegetation types with the aim to create formalised definitions of all Slovakian mire plant associations. We defined 21 groups of species with the statistical tendency of joint occurrences in vegetation. These groups differed substantially in their position along the pH/calcium gradient. We further defined 24 plant associations according to presence and/or absence of certain groups and/or strong dominance of some species. Only six traditional plant associations were not possible to be reproduced this way. We applied our formalised definitions to the regional data set of mires from the surrounding of the Vysoké Tatry Mts. Combined with frequency-positive fidelity index this method has led to the classification of the majority of vegetation plots into ten associations. When the vegetation types obtained from Cocktail-based classification and from cluster analysis were compared with respect to measured pH and conductivity in the study region, 82% of pairs differed significantly either in pH or in water conductivity in the former classification and 69% in the latter one.

User Behaviors and Knowledge Exchange in Health Infomediary

2018 ◽  
pp. 213-233
Author(s):  
Dobin Yim ◽  
Jiban Khuntia ◽  
Young Anna Argyris
Keyword(s):  
Cluster Analysis ◽  
Cosmetic Surgery ◽  
Knowledge Exchange ◽  
Large Data ◽  
Two Dimensions ◽  
Data Set ◽  
User Behaviors ◽  
Large Data Set ◽  
Similarities And Differences

Online health infomediaries have the objective of knowledge exchange between participants. Visitor contribution is an important factor for the success of the infomediaries. Providers engaged with infomediaries need visitor identification for reputational incentives. However, identification or classification of visitors in online health infomediaries is sparse in literature. This chapter proposes two dimensions of participation, the intention and intensity levels of visitors, to conceptualize four user categories: community supporters, experiencer providers, knowledge questors, and expertise contributors. The authors validate these categories using a unique large data set collected from a health infomediary for cosmetic surgery, and consisting of 162,598 observed activities of 44,350 visitors, at different participation levels in the year 2012-13. They use cluster analysis to describe similarities and differences among the four user categories. Practice implications are discussed.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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