Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions
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Publication:1704742
DOI10.1007/s10827-013-0455-7zbMath1382.92092OpenAlexW2079893016WikidataQ37249035 ScholiaQ37249035MaRDI QIDQ1704742
Dong Song, Vasilis Z. Marmarelis, Catherine Y. Tu, Theodore W. Berger, Haonan Wang, Robert E. Hampson, Sam A. Deadwyler
Publication date: 13 March 2018
Published in: Journal of Computational Neuroscience (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc3805829
temporal codingbasis functionsparsitygeneralized linear modelpenalized likelihoodspike trainsfunctional connectivity
Applications of statistics to biology and medical sciences; meta analysis (62P10) General biostatistics (92B15) Neural biology (92C20)
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- The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis
- Estimating the dimension of a model
- Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
- On calculating with B-splines
- A Spike-Train Probability Model
- Discrete Time Rescaling Theorem: Determining Goodness of Fit for Discrete Time Statistical Models of Neural Spiking
- Nonparametric Modeling of Neural Point Processes via Stochastic Gradient Boosting Regression
- Identifying Functional Connectivity in Large-Scale Neural Ensemble Recordings: A Multiscale Data Mining Approach
- Semiparametric methods for evaluating risk prediction markers in case-control studies
- Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
- A Statistical View of Some Chemometrics Regression Tools
- Estimation of Entropy and Mutual Information
- Model Selection and Estimation in Regression with Grouped Variables
- Orthogonal functionals of the Poisson process
- Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity