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The time-rescaling theorem and its application to neural spike train data analysis., , , , and . Neural Comput., 14 (2): 325--346 (February 2002)Detection of bursts in extracellular spike trains using hidden semi-Markov point process models., , , and . J. Comput. Neurosci., 29 (1-2): 203-212 (2010)Information In The Non-Stationary Case, , and . CoRR, (2008)Spike Train Probability Models for Stimulus-Driven Leaky Integrate-and-Fire Neurons., and . Neural Comput., 20 (7): 1776-1795 (2008)A Framework for Evaluating Pairwise and Multiway Synchrony Among Stimulus-Driven Neurons., and . Neural Comput., 24 (8): 2007-2032 (2012)Estimating Learning Effects: A Short-Time Fourier Transform Regression Model for MEG Source Localization., , and . MLINI@NIPS, volume 9444 of Lecture Notes in Computer Science, page 69-82. Springer, (2014)Accounting for network effects in neuronal responses using L1 regularized point process models., , , and . NIPS, page 1099-1107. Curran Associates, Inc., (2010)Comparison of brain-computer interface decoding algorithms in open-loop and closed-loop control., , , , , and . J. Comput. Neurosci., 29 (1-2): 73-87 (2010)Statistical analysis of temporal evolution in single-neuron firing rates., , , , and . Biostatistics, 3 (1): 1--20 (March 2002)A note on the interpretation of the Bahadur bound and the rate of convergence of the maximum likelihood estimator, and . Statistics & Probability Letters, 2 (5): 269--273 (October 1984)