| Generating spike trains with specified correlation coefficients JH Macke, P Berens, AS Ecker, AS Tolias, M Bethge Neural Computation 21 (2), 397-423, 2009 | 212 | 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 | 184 | 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 | 147 | 2015 |
| 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 | 143 | 2013 |
| 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 | 136 | 2016 |
| 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 | 136 | 2015 |
| Quantifying the effect of intertrial dependence on perceptual decisions I Fründ, FA Wichmann, JH Macke Journal of vision 14 (7), 9-9, 2014 | 130 | 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 | 98 | 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 | 77 | 2008 |
| Bayesian inference for generalized linear models for spiking neurons S Gerwinn, JH Macke, M Bethge Frontiers in computational neuroscience 4, 2010 | 71 | 2010 |
| 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 | 69 | 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, 1682-1690, 2012 | 64 | 2012 |
| 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 | 60* | 2018 |
| Flexible statistical inference for mechanistic models of neural dynamics JM Lueckmann, PJ Goncalves, G Bassetto, K Öcal, M Nonnenmacher, ... Advances in neural information processing systems, 1289-1299, 2017 | 55 | 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, 343-351, 2014 | 49 | 2014 |
| 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 | 49 | 2012 |
| Bayesian inference for spiking neuron models with a sparsity prior S Gerwinn, J Macke, M Seeger, M Bethge Advances in neural information processing systems 20, 529–536, 2008 | 38 | 2008 |
| 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 | 37* | 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 | 37 | 2015 |
| 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 | 36 | 2019 |
Matthias BethgeTübingen University & Amazon ScholarVerified email at bethgelab.org
