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Generating Spike Trains with Specified Correlation Coefficients.

, , , , and . Neural Computation, 21 (2): 397-423 (2009)

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Empirical models of spiking in neural populations., , , , , and . NIPS, page 1350-1358. (2011)Unsupervised learning of a steerable basis for invariant image representations., , and . Human Vision and Electronic Imaging, volume 6492 of SPIE Proceedings, page 64920C. SPIE, (2007)Intrinsic dimension of data representations in deep neural networks., , , and . NeurIPS, page 6109-6119. (2019)Bayesian Inference for Spiking Neuron Models with a Sparsity Prior., , , and . NIPS, page 529-536. Curran Associates, Inc., (2007)Statistical Analysis of Multi-Cell Recordings: Linking Population Coding Models to Experimental Data., , and . Front. Comput. Neurosci., (2011)Fast amortized inference of neural activity from calcium imaging data with variational autoencoders., , , , , and . NIPS, page 4024-4034. (2017)Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations., , and . NIPS, page 5702-5712. (2017)Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations., , and . Front. Comput. Neurosci., (2010)Inferring neural population dynamics from multiple partial recordings of the same neural circuit., , , , , , and . NIPS, page 539-547. (2013)Unlocking neural population non-stationarities using hierarchical dynamics models., , and . NIPS, page 145-153. (2015)