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Neural Computation|June 13, 2014
Neuronal spike train entropy estimation by history clusteringNicholas Watters, George N ReekeThe Behavioral and Brain Sciences|November 29, 2019
Not just a bad metaphor, but a little piece of a big bad metaphorGeorge N ReekeThe Behavioral and Brain Sciences|February 5, 2008
Modelling criteria: Not just for robotsGeorge N ReekeAnesthesiology|April 18, 2018
γ-Aminobutyric Acid Type A Receptor Potentiation Inhibits Learning in a Computational Network ModelKingsley P Storer, George N ReekeNeural Computation|April 9, 2004
Estimating the temporal interval entropy of neuronal dischargeGeorge N Reeke, Allan D CoopComputational Intelligence and Neuroscience|June 16, 2012
Quantitative tools for examining the vocalizations of juvenile songbirdsCameron D Wellock, George N ReekeAnesthesiology|August 21, 2012
γ-Aminobutyric acid receptor type A receptor potentiation reduces firing of neuronal assemblies in a computational cortical modelKingsley P Storer, George N ReekeJournal of Integrative Neuroscience|September 15, 2004
Control of neuronal discharge timing by afferent fiber number and the temporal pattern of afferent impulsesAllan D Coop, George N ReekeNeural Computation|November 20, 2009
A continuous entropy rate estimator for spike trains using a K-means-based context treeTiger W Lin, George N ReekeProceedings of the National Academy of Sciences of the United States of America|October 9, 2013
Network model of top-down influences on local gain and contextual interactions in visual cortexValentin Piëch, Wu Li, George N Reeke, et al.Pageof 2