Correcting for the sampling bias problem in spike train information measures.

By Stefano Panzeri, Riccardo Senatore, Marcelo A. Montemurro, Rasmus S. Petersen. Journal of Neurophysiology, 2007

449 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f3e178e184ca1b051b4eb5ace0326690646e7c09

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Statistical and Nonlinear Physics

Citations in the corpus

Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. (2009, unknown): “Page: 8 previously (Montemurro et al., 2008; Montemurro et al., 2007; Panzeri et al., 2007).”
Gamma-band synchronization in the neocortex: novel analysis methods and their application to sensory and motivational systems (2013, background): “Yet, the mutual information function was originally developed for discrete variables whose probability distributions are a priori known, and its direct estimate suffers from very strong statistical bias, which makes application to neural data difficult (Panzeri et al., 2007; Nemenman et al., 2004).”
Encoding of Naturalistic Stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons (2008, methodology): “In brief, the bias estimation of the information contained in the multidimensional responses was greatly reduced at the very source, and made negative, by using the ‘‘shuffling’’ technique described in [83].”
A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula (2016, background): “A wide variety of approaches have been proposed to address this problem [Paninski, 2003; Panzeri et al., 2007].”
Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information (2008, methodology): “When computing the information I(S; Rf 1Rf 2) conveyed by the joint observation of two powers, we also applied the “shuffling procedure” described by Montemurro et al. (2007) and Panzeri et al. (2007), which greatly reduces the bias of multidimensional information estimates.”
Frontiers in Integrative Neuroscience Integrative Neuroscience (unknown): “Practically, we applied the quadratic extrapolation technique to control for potential statistical biases (Panzeri et al., 2007).”
Equitability, mutual information, and the maximal information coefficient (2013, background): “Despite these difficulties, a variety of practical estimation techniques have been developed and tested (11, 12).”
Bayesian Active Learning for Classification and Preference Learning (2011, background): “…parameter space (exponentially hard with dimensionality), or sampling (from which it is notoriously hard to estimate entropies without introducing bias [Panzeri and Petersen, 2007]), these papers make Gaussian or low dimensional approximations and calculate the entropy of the approximate posterior.”