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
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
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
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
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
Bayesian inference for generalized linear models for spiking neurons
S Gerwinn, JH Macke, M Bethge
Frontiers in computational neuroscience 4, 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
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
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
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
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
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
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
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
A Speiser, J Yan, EW Archer, L Buesing, SC Turaga, JH Macke
Advances in Neural Information Processing Systems, 4024-4034, 2017
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
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