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Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions.

, , and . Neural Comput., 16 (2): 223-250 (2004)

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Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions., , and . Neural Comput., 16 (2): 223-250 (2004)Quantifying Information Conveyed by Large Neuronal Populations., and . Neural Comput., 31 (6): 1015-1047 (2019)Using Global t-SNE to Preserve Intercluster Data Structure., and . Neural Comput., 34 (8): 1637-1651 (2022)25th annual computational neuroscience meeting: CNS-2016, , , , , , , , , and 1 other author(s). BMC neuroscience, 17, page 1--112. BioMed Central, (2016)Characterizing Responses of Translation-Invariant Neurons to Natural Stimuli: Maximally Informative Invariant Dimensions., , , and . Neural Comput., 24 (9): 2384-2421 (2012)Better than least squares: comparison of objective functions for estimating linear-nonlinear models.. NIPS, page 1305-1312. Curran Associates, Inc., (2007)Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models., , , and . PLoS Comput. Biol., (2011)Contextual modulation of V1 receptive fields depends on their spatial symmetry., and . J. Comput. Neurosci., 26 (2): 203-218 (2009)Double-Gabor Filters Are Independent Components of Small Translation-Invariant Image Patches., , and . Neural Comput., 25 (4): 922-939 (2013)Spike Triggered Covariance in Strongly Correlated Gaussian Stimuli., , , and . PLoS Comput. Biol., (2013)