A common goodness-of-fit framework for neural population models using marked point process time-rescaling

DOI10.1007/s10827-018-0698-4zbMath1402.92120OpenAlexW2950731900WikidataQ57294737 ScholiaQ57294737MaRDI QIDQ1628363

Long Tao, Uri T. Eden, Kensuke Arai, Karoline E. Weber

Publication date: 4 December 2018

Published in: Journal of Computational Neuroscience (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10827-018-0698-4

zbMATH Keywords

goodness-of-fitspike trainsneural modelingKS plotsneural population activitytime-rescaling

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Neural biology (92C20)

Related Items (2)

Assessing Goodness-of-Fit in Marked-Point Process Models of Neural Population Coding via Time and Rate Rescaling ⋮ popTRT

Uses Software

• Matlab
• spatstat
• spatial
• Python
• GitHub

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis
• A multivariate uniformity test for the case of unknown support
• An empirical study of tests for uniformity in multidimensional data
• A characterization of the spatial Poisson process and changing time
• Analysis of neural data
• A Spike-Train Probability Model
• Applying the Multivariate Time-Rescaling Theorem to Neural Population Models
• A General Likelihood Framework for Characterizing the Time Course of Neural Activity
• A State-Space Analysis for Reconstruction of Goal-Directed Movements Using Neural Signals
• Sampling-Based Approaches to Calculating Marginal Densities
• Decoding Movement Trajectories Through a T-Maze Using Point Process Filters Applied to Place Field Data from Rat Hippocampal Region CA1
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• A simple proof of the multivariate random time change theorem for point processes
• Population Coding and Decoding in a Neural Field: A Computational Study
• Rescaling Marked Point Processes
• An Introduction to the Theory of Point Processes
• Estimating a State-Space Model from Point Process Observations
• Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering
• Testing multivariate uniformity: The distance‐to‐boundary method
• Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter
• Integrability of Expected Increments of Point Processes and a Related Random Change of Scale

This page was built for publication: A common goodness-of-fit framework for neural population models using marked point process time-rescaling