A General Likelihood Framework for Characterizing the Time Course of Neural Activity

DOI10.1162/NECO_a_00185zbMath1231.92024OpenAlexW1988745698WikidataQ51550629 ScholiaQ51550629MaRDI QIDQ3116943

M. J. Prerau, Uri T. Eden

Publication date: 14 February 2012

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_00185

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Probabilistic models, generic numerical methods in probability and statistics (65C20) Neural biology (92C20)

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Cites Work

• Unnamed Item
• Generalized linear models
• On the consistency of cross-validation in kernel nonparametric regression
• On cross-validation in kernel and partitioning regression estimation.
• Cross-validation in nonparametric regression with outliers
• Bayesian curve-fitting with free-knot splines
• Cross-Validation of Regression Models
• A Method for Selecting the Bin Size of a Time Histogram
• Introduction to Theoretical Neurobiology
• An Introduction to the Theory of Point Processes
• Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering
• Valuations for Spike Train Prediction
• Cross-validation Bandwidth Matrices for Multivariate Kernel Density Estimation
• On Estimation of a Probability Density Function and Mode

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