The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis

By Emery N. Brown, Riccardo Barbieri, Valérie Ventura, Robert E. Kass, Loren M. Frank. Neural Computation, 2002

601 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/006a34dcdfc63c5b7a2cf4d0b76cecedbb4b93d9

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Maximum likelihood estimation of cascade point-process neural encoding models (2004, background): “…of the IF–GLM are much more precise than are those of the simple LNP model, in large part because the refractory effects of the post-spike current h serve to enhance the reproducibility of the resulting spike-train (Joeken et al 1997, Berry and Meister 1998, Reich et al 1998, Brown et al 2002).”
Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering (2004, methodology): “To assess goodness-of-t, the observed ISIs were rescaled using the timerescaling theorem (Brown et al., 2002) in order to compute the KS statistic for each algorithm.”
Estimating a State-Space Model from Point Process Observations (2003, methodology): “Therefore, we can construct a Kolmogorov-Smirnov (K-S) test to measure agreement between the zjs and the uniform probability density (Barbieri et al., 2001; Brown et al., 2002).”
A Space–Time Conditional Intensity Model for Evaluating a Wildfire Hazard Index (2005, methodology): “One-dimensional residual analysis via the rescaling method has been successfully applied in a wide variety of applications (e.g. Berman, 1983; Ogata, 1988; Diggle, 1990; Rathbun, 1993; Brown et al., 2001).”
A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. (2005, methodology): “One alternative solution to this problem is to apply the time-rescaling theorem (Papangelou, 1972; Ogata, 1988; Brown et al., 2002) to transform point processes like spike trains into continuous measures appropriate for goodness-of-fit assessment.”
Mixture Modeling for Marked Poisson Processes (2010, methodology): “For instance, in the context of neuronal data analysis, Brown et al. (2001) used standard tests and quantile-quantile (Q-Q) plots to measure agreement of the estimated ui with the uniform distribution on (0, 1).”
New experiences enhance coordinated neural activity in the hippocampus. (2008, unknown): “We therefore derived an adaptive filtering algorithm (Brown et al., 2001, 2002; Frank et al., 2002) that estimated the changing relationship between position x t , theta phase q t , and firing rate l(t).”