| Generating spike trains with specified correlation coefficients JH Macke, P Berens, AS Ecker, AS Tolias, M Bethge Neural Computation 21 (2), 397-423, 2009 | 189 | 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 | 152 | 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, 2015 | 130 | 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, 2013 | 126 | 2013 |
| Quantifying the effect of intertrial dependence on perceptual decisions I Fründ, FA Wichmann, JH Macke Journal of vision 14 (7), 9-9, 2014 | 106 | 2014 |
| 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 | 104 | 2015 |
| 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 | 94 | 2011 |
| 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 | 82 | 2016 |
| 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 | 76 | 2008 |
| 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 | 65 | 2008 |
| Bayesian inference for generalized linear models for spiking neurons S Gerwinn, JH Macke, M Bethge Frontiers in computational neuroscience 4, 2010 | 63 | 2010 |
| 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, 1682-1690, 2012 | 51 | 2012 |
| 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 | 41 | 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 | 39 | 2018 |
| 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, 343-351, 2014 | 39 | 2014 |
| 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 | 36 | 2017 |
| 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 | 36 | 2008 |
| 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 | 32 | 2015 |
| Inferring neural population dynamics from multiple partial recordings of the same neural circuit S Turaga, L Buesing, AM Packer, H Dalgleish, N Pettit, M Hausser, ... Advances in Neural Information Processing Systems, 539-547, 2013 | 27 | 2013 |
| Low error discrimination using a correlated population code G Schwartz, J Macke, D Amodei, H Tang, MJ Berry Journal of neurophysiology 108 (4), 1069-1088, 2012 | 26 | 2012 |
Matthias BethgeTübingen University & Amazon ScholarVerified email at bethgelab.org
