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Convolutional spike-triggered covariance analysis for neural subunit models.

, , and . NIPS, page 793-801. (2015)

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Bayesian Extensions of Kernel Least Mean Squares., , and . CoRR, (2013)Strictly Positive-Definite Spike Train Kernels for Point-Process Divergences., , , and . Neural Comput., 24 (8): 2223-2250 (2012)Information Geometry of Orthogonal Initializations and Training, and . International Conference on Learning Representations, (2020)Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling., , , and . CoRR, (2018)Kernel Methods on Spike Train Space for Neuroscience: A Tutorial., , , , and . IEEE Signal Process. Mag., 30 (4): 149-160 (2013)Bayesian entropy estimation for binary spike train data using parametric prior knowledge., , and . NIPS, page 1700-1708. (2013)A novel family of non-parametric cumulative based divergences for point processes., , , , , , and . NIPS, page 2119-2127. Curran Associates, Inc., (2010)Information Geometry of Orthogonal Initializations and Training., and . ICLR, OpenReview.net, (2020)Multistep inference for generalized linear spiking models curbs runaway excitation., and . NER, page 613-616. IEEE, (2017)Bayesian Entropy Estimation for Countable Discrete Distributions, , and . (2013)cite arxiv:1302.0328Comment: 38 pages LaTeX. Revised and resubmitted to JMLR.