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Combined head phantom and neural mass model validation of effective connectivity measures
2019
Journal of Neural Engineering
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While many connectivity estimators are available, the efficacy of these measures has not been rigorously validated in real-world scenarios. ...
Due to its high temporal resolution, electroencephalography (EEG) has become a promising tool for quantifying cortical dynamics and effective connectivity in a mobile setting. ...
Acknowledgments We would like to thank Jean Vettel and Manuel Vindiola at the Army Research Lab for providing the neural mass model and assisting with its implementation. ...
doi:10.1088/1741-2552/aaf60e
pmid:30523864
pmcid:PMC6448772
fatcat:6j3wdyrbl5egpmtlcnxyrarrne
Mapping distinct timescales of functional interactions among brain networks
2019
Advances in Neural Information Processing Systems
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Due to the slow sampling rate of fMRI, it is widely held that GC produces spurious and unreliable estimates of functional connectivity when applied to fMRI data. ...
resonance imaging (fMRI) data. ...
We would like to thank Hritik Jain for help with data analysis. ...
pmid:31285649
pmcid:PMC6614036
fatcat:hgzscu353vdfhkxyqbfnkos7bm
Pruning neural network for architecture optimization applied to near-infrared reflectance spectroscopic measurements. Determination of the nitrogen content in wheat leaves
1999
The Analyst
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This network pruning procedure was applied for estimating the nitrogen contents in wheat leaves, using near-infrared diffuse reflectance spectroscopy. ...
Although the comparison was performed for one data set, the pruning procedure has the advantage of introducing an automatic architecture optimization, which is cumbersome when performed by the other neural ...
Collins for critically reading the manuscript. ...
doi:10.1039/a905570c
fatcat:ew5lagyt75af7jqtq6tvb7nx44
Learning-based AC-OPF Solvers on Realistic Network and Realistic Loads
[article]
2022
arXiv
pre-print
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Trained on samples with bus loads generated from a fitted multivariate normal distribution, our learning-based AC-OPF solver achieves 0.13% cost optimality gap, 99.73% feasibility rate, and 38.62 times ...
Feasibility-optimized end-to-end deep neural network models are trained and tested on the constructed dataset. ...
It is a problem faced daily by power companies that, if solved reliably in near real-time, could result in tremendous savings in operation costs. ...
arXiv:2205.09452v1
fatcat:yo2pqguscrbqdnpvvqf5t3i5jy
A state-space model for dynamic functional connectivity
2019
2019 53rd Asilomar Conference on Signals, Systems, and Computers
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Dynamic functional connectivity (DFC) analysis involves measuring correlated neural activity over time across multiple brain regions. ...
To address this issue, we propose a multivariate stochastic volatility model for estimating DFC inspired by recent work in econometrics research. ...
Therefore, DFC analysis involves estimating a time-series of pairwise correlations from multivariate neural data. ...
doi:10.1109/ieeeconf44664.2019.9048807
pmid:32801606
pmcid:PMC7425228
fatcat:imb33si7wrfc5itmbnisauhio4
Multi-Connection Pattern Analysis: Decoding the Representational Content of Neural Communication
[article]
2016
bioRxiv
pre-print
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The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit ...
Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. ...
Acknowledgements We would like to thank the patient for participating in the iEEG experiments, and Dr. R. Mark Richardson, Michael Ward and the epilepsy monitoring unit staff, Cheryl Plummer, ...
doi:10.1101/046441
fatcat:me3v7ymza5h2blvhbzzkcbtciq
BSMART: A Matlab/C toolbox for analysis of multichannel neural time series
2008
Neural Networks
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We have developed a Matlab/C toolbox, Brain-SMART (System for Multivariate AutoRegressive Time series, or BSMART), for spectral analysis of continuous neural time series data recorded simultaneously from ...
Available functions include time series data importing/exporting, preprocessing (normalization and trend removal), AutoRegressive (AR) modeling (multivariate/bivariate model estimation and validation), ...
Richard Nakamura of the National Institute of Mental Health for providing the sample data set of event-related LFP time series used in BSMART. ...
doi:10.1016/j.neunet.2008.05.007
pmid:18599267
pmcid:PMC2585694
fatcat:zjgggt7cafajvje334w7lmif4y
Using Neural Networks for Simulating and Predicting Core-End Temperatures in Electrical Generators: Power Uprate Application
2015
World Journal of Engineering and Technology
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The model was successfully applied to estimate temperatures associated to 108% power and used to extrapolate temperature values for a power uprate to 113.48%. ...
The mathematical model developed was based on an artificial intelligence technique, more specifically neural networks. ...
that neural network temperature output values match real values. ...
doi:10.4236/wjet.2015.31001
fatcat:od4wk7wexjazxa6xti2rnkgou4
Similarity representation of pattern-information fMRI
2013
Chinese Science Bulletin
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Representational similarity analysis (RSA) is a rapidly developing multivariate platform to investigate the structure of neural activities. ...
This review summarizes dissimilarity/similarity definition of RSA, introduces how to derive the dissimilarity structure in neural response pattern, and carry out connectivity analysis based on RSA platform ...
in the GLM model; then the estimated coefficient of GLM (beta value) for each predictor and each voxel is obtained and forms the basis for computation of the representational dissimilarities. ...
doi:10.1007/s11434-013-5743-0
fatcat:af6oc4nmnjftrapukernlxkv6u
Analysis of Time Series Prediction using Recurrent Neural Networks
2019
International Journal of Computer Applications
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can provide the vital help and safety or the advancement for the change, though the future is uncertain but people must know their future as near it could be to the future. ...
Based on the research this paper contains analytical data of recurrent neural network and its use with time series alongside the experimental data analysis of weather forecast and financial forecast data ...
ARIMA was feeded with same parameters and feature spaces as LSTM and GRU for same collected rendered data to predict the values for Bitcoin which found to have RMSE of 255.90 and had training time of 0.6169 ...
doi:10.5120/ijca2019918732
fatcat:u2ighcayznegbcjty42d4j7mle
Efficient estimation of neural weights by polynomial approximation
1999
IEEE Transactions on Information Theory
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It has been known for some years that the uniformdensity problem for forward neural networks has a positive answer: Any real-valued, continuous function on a compact subset of R R R d can be uniformly ...
This theorem, in turn, is a consequence of a close relationship between neural networks of nearly exponential type and multivariate algebraic and exponential polynomials. ...
ACKNOWLEDGMENT The author thanks Herr Christoph Pesch for his help with the SNNS and a referee for pointing out [2] and [8] and a weakness of Algorithm 4.1 that led to the formulation of Algorithm ...
doi:10.1109/18.771153
fatcat:tvp5brxe2fcepdbennkblomyfe
A Hybrid Model of Bidirectional Long-Short Term Memory and CNN for Multivariate Time Series Classification of Remote Sensing Data
2021
Journal of Computer Science
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The proposed Conv-BiLSTM is carried out for classifying the land cover multivariate time series of Landsat 8 satellite images. ...
The traditional modeling classifiers are complicated patterns and are incompetent to capture the dependencies of multivariate time series data. ...
band of twenty three time series data for different classes of land cover. ...
doi:10.3844/jcssp.2021.789.802
fatcat:7ck4p675xfbx5iqjmwlrxl2ome
A state-space model for dynamic functional connectivity
[article]
2019
arXiv
pre-print
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Dynamic functional connectivity (DFC) analysis involves measuring correlated neural activity over time across multiple brain regions. ...
To address this issue, we propose a multivariate stochastic volatility model for estimating DFC inspired by recent work in econometrics research. ...
Therefore, DFC analysis involves estimating a time-series of pairwise correlations from multivariate neural data. ...
arXiv:1912.05595v1
fatcat:mn6gtnhduffqpd7wrfesgvvipu
Deep Learning Framework for Physical Internet Hubs Inbound Containers Forecasting
2022
International Journal of Advanced Computer Science and Applications
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Second, the framework uses convolutional neural network (CNN) and recurrent neural network (RNN) as training networks for the historical time series data in two techniques. ...
The proposed framework uses univariate and multivariate time series analysis to explore the maximum forecasting outcomes. ...
Multivariate analysis is more suitable for real life applications because of its high conclusion accuracy. ...
doi:10.14569/ijacsa.2022.0130327
fatcat:tms2jmmwt5gdjmxrqdwdsj2fl4
Torus graphs for multivariate phase coupling analysis
2020
Annals of Applied Statistics
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Torus graphs thus unify multivariate analysis of circular data and present fertile territory for future research. ...
For such vectors we present here a class of graphical models, which we call torus graphs, based on the full exponential family with pairwise interactions. ...
The authors thank the reviewers for their valuable feedback. Robert E. Kass, N. Klein and J. ...
doi:10.1214/19-aoas1300
fatcat:m6wgyiktm5h7fjihhukkverq3a
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