Jakob Macke
Jakob Macke
Machine Learning in Science, Tübingen University
Verified email at uni-tuebingen.de - Homepage
Cited by
Cited by
Generating spike trains with specified correlation coefficients
JH Macke, P Berens, AS Ecker, AS Tolias, M Bethge
Neural Computation 21 (2), 397-423, 2009
Empirical models of spiking in neural populations
JH Macke, L Buesing, JP Cunningham, BM Yu, KV Shenoy, M Sahani
Advances in Neural Information Processing Systems 24, 2011
Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression
R Küffner, N Zach, R Norel, J Hawe, D Schoenfeld, L Wang, G Li, L Fang, ...
Nature biotechnology 33 (1), 51-57, 2015
Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data
HH Schütt, S Harmeling, JH Macke, FA Wichmann
Vision research 122, 105-123, 2016
Inferring decoding strategies from choice probabilities in the presence of correlated variability
RM Haefner, S Gerwinn, JH Macke, M Bethge
Nature neuroscience 16 (2), 235-242, 2013
Neural population coding: combining insights from microscopic and mass signals
S Panzeri, JH Macke, J Gross, C Kayser
Trends in cognitive sciences 19 (3), 162-172, 2015
Quantifying the effect of intertrial dependence on perceptual decisions
I Fründ, FA Wichmann, JH Macke
Journal of vision 14 (7), 9-9, 2014
Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity
JH Macke, M Opper, M Bethge
Physical Review Letters 106 (20), 208102, 2011
Contour-propagation algorithms for semi-automated reconstruction of neural processes
JH Macke, N Maack, R Gupta, W Denk, B Schölkopf, A Borst
Journal of neuroscience methods 167 (2), 349-357, 2008
Bayesian inference for generalized linear models for spiking neurons
S Gerwinn, JH Macke, M Bethge
Frontiers in computational neuroscience 4, 2010
Community-based benchmarking improves spike rate inference from two-photon calcium imaging data
P Berens, J Freeman, T Deneux, N Chenkov, T McColgan, A Speiser, ...
PLoS computational biology 14 (5), e1006157, 2018
Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys
S Ku, A Gretton, J Macke, NK Logothetis
Magnetic resonance imaging 26 (7), 1007-1014, 2008
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data
L Buesing, JH Macke, M Sahani
Advances in Neural Information Processing Systems 25: 26th Conference on …, 2013
Flexible statistical inference for mechanistic models of neural dynamics
JM Lueckmann, PJ Goncalves, G Bassetto, K Öcal, M Nonnenmacher, ...
arXiv preprint arXiv:1711.01861, 2017
Low-dimensional models of neural population activity in sensory cortical circuits
EW Archer, U Koster, JW Pillow, JH Macke
Advances in Neural Information Processing Systems 27: 28th Conference on …, 2015
Automatic Posterior Transformation for Likelihood-Free Inference
Greenberg D. S., Nonnenmacher M., Macke J. H.
Proceedings of the 36th International Conference on Machine Learning, ICML …, 2019
Learning stable, regularised latent models of neural population dynamics
L Buesing, JH Macke, M Sahani
Network: Computation in Neural Systems 23 (1-2), 24-47, 2012
Analyzing biological and artificial neural networks: challenges with opportunities for synergy?
DGT Barrett, AS Morcos, JH Macke
Current opinion in neurobiology 55, 55-64, 2019
Estimating state and parameters in state space models of spike trains
JH Macke, L Buesing, M Sahani, Z Chen
Advanced state space methods for neural and clinical data 137, 2015
Intrinsic dimension of data representations in deep neural networks
DZ A Ansuini, A Laio, JH Macke
Advances in Neural Information Processing Systems 32 (Neurips 2019), 2019
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