Jakob H Macke - Citegraph

Author Info

Name	Affiliation	Papers
JAKOB H MACKE	Max Planck Inst Biol Cybernet, D-72076 Tubingen, Germany	25
Collaborators	Citations	PageRank
83	158	14.15
Referers	Referees	References
391	545	203

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100545

Publications (25 rows)

Collaborators (83 rows)

Referers (100 rows)

Referees (100 rows)

Title	Citations	PageRank	Year
GATSBI: Generative Adversarial Training for Simulation-Based Inference	0	0.34	2022
Group equivariant neural posterior estimation	0	0.34	2022
Benchmarking Simulation-Based Inference	0	0.34	2021
Inference of a mesoscopic population model from population spike trains	0	0.34	2020
Intrinsic dimension of data representations in deep neural networks.	0	0.34	2019
Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.	5	0.50	2018
Analyzing biological and artificial neural networks: challenges with opportunities for synergy?	0	0.34	2018
Likelihood-free inference with emulator networks.	1	0.36	2018
Flexible statistical inference for mechanistic models of neural dynamics.	3	0.39	2017
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders	3	0.43	2017
Signatures of criticality arise from random subsampling in simple population models.	4	0.40	2017
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations	2	0.40	2017
A Bayesian model for identifying hierarchically organised states in neural population activity.	0	0.34	2014
Low-dimensional models of neural population activity in sensory cortical circuits.	6	0.48	2014
Inferring neural population dynamics from multiple partial recordings of the same neural circuit.	8	0.57	2013
Estimation Bias in Maximum Entropy Models.	0	0.34	2013
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data.	9	0.68	2012
How biased are maximum entropy models?	1	0.39	2011
Empirical models of spiking in neural populations.	26	1.43	2011
Statistical Analysis of Multi-Cell Recordings: Linking Population Coding Models to Experimental Data.	9	0.64	2011
Bayesian inference for generalized linear models for spiking neurons.	16	0.89	2010
Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations.	6	0.47	2010
Generating spike trains with specified correlation coefficients	55	2.50	2009
Bayesian estimation of orientation preference maps.	0	0.34	2009
Bayesian population decoding of spiking neurons.	4	0.59	2009