Mutual Information Expansion for Studying the Role of Correlations in Population Codes: How Important Are Autocorrelations? | Citedby Results | Neural Computation | MIT Press

Skip to Main Content

Skip Nav Destination

Mutual Information Expansion for Studying the Role of Correlations in Population Codes: How Important Are Autocorrelations?

Neural Computation (2008) 20 (11): 2662–2695.

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

A Causal Perspective on the Analysis of Signal and Noise Correlations and Their Role in Population Coding

Daniel Chicharro

Neural Computation (2014) 26 (6): 999.

A Maximum Entropy Test for Evaluating Higher-Order Correlations in Spike Counts

Arno Onken
Valentin Dragoi
Klaus Obermayer
Emery N. Brown

PLoS Computational Biology (2012) 8 (6): e1002539.

Random Bin for Analyzing Neuron Spike Trains

Shinichi Tamura
Tomomitsu Miyoshi
Hajime Sawai
Yuko Mizuno-Matsumoto

Computational Intelligence and Neuroscience (2012) 2012: 1.

Trial-to-trial variability in the responses of neurons carries information about stimulus location in the rat whisker thalamus

Alessandro Scaglione
Karen A. Moxon
Juan Aguilar
Guglielmo Foffani

Proceedings of the National Academy of Sciences (2011) 108 (36): 14956.

Information-theoretic methods for studying population codes

Robin A.A. Ince
Riccardo Senatore
Ehsan Arabzadeh
Fernando Montani
Mathew E. Diamond
Stefano Panzeri

Neural Networks (2010) 23 (6): 713.

General Poisson Exact Breakdown of the Mutual Information to Study the Role of Correlations in Populations of Neurons

A. Scaglione
K. A. Moxon
G. Foffani

Neural Computation (2010) 22 (6): 1445.

A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

Cesare Magri
Kevin Whittingstall
Vanessa Singh
Nikos K Logothetis
Stefano Panzeri

BMC Neuroscience (2009) 10 (1)