Suchergebnisse

Nonnenmacher, M.; Behrens, C.; Berens, P.; Bethge, M.; Macke, J.: Signatures of criticality arise from random subsampling in simple population models. PLoS Computational Biology 13 (10), S. 1 - 23 (2017)

Schütt, H.; Harmeling, S.; Macke, J.; Wichmann, F.: Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data. Vision Research 122, S. 105 - 123 (2016)

Panzeri, S.; Macke, J.; Gross, J.; Kayser, C.: Neural population coding: combining insights from microscopic and mass signals. Trends in Cognitive Sciences 19 (3), S. 162 - 172 (2015)

Küffner, R.; Zach, N.; Norel, R.; Hawe, J.; Schoenfeld, D.; Wang, L.; Li, G.; Fang, L.; Mackey, L.; Hardiman, O. et al.; Cudkowicz, M.; Sherman, A.; Ertaylan, G.; Grosse-Wentrup, M.; Hothorn, T.; van Ligtenberg, J.; Macke, J.; Meyer, T.; Schölkopf, B.; Tran, L.; Vaughan, R.; Stolovitzky, G.; Leitner, M.: Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression. Nature Biotechnology 33 (1), S. 51 - 57 (2015)

Park, M.; Macke, J.: Hierarchical models for neural population dynamics in the presence of non-stationarity. - submitted (2015)

Fründ, I.; Wichmann, F.; Macke, J.: Quantifying the effect of intertrial dependence on perceptual decisions. Journal of Vision 14 (7:9), S. 1 - 16 (2014)

Watanabe, M.; Bartels, A.; Macke, J.; Murayama, Y.; Logothetis, N.: Temporal Jitter of the BOLD Signal Reveals a Reliable Initial Dip and Improved Spatial Resolution. Current Biology 23 (21), S. 2146 - 2150 (2013)

Macke, J.; Murray, I.; Latham, P.: Estimation bias in maximum entropy models. Entropy 15 (8), S. 3109 - 3219 (2013)

Haefner, R.; Gerwinn, S.; Macke, J.; Bethge, M.: Inferring decoding strategies from choice probabilities in the presence of correlated variability. Nature Neuroscience 16 (2), S. 235 - 242 (2013)

Schwartz, G.; Macke, J.; Amodei, D.; Tang, H.; Berry II, M.: Low Error Discrimination using a Correlated Population Code. Journal of Neurophysiology 108 (4), S. 1069 - 1088 (2012)

Buesing, L.; Macke, J.; Sahani, M.: Learning stable, regularised latent models of neural population dynamics. Network 23 (1-2), S. 24 - 47 (2012)

Macke, J.; Berens, P.; Bethge, M.: Statistical analysis of multi-cell recordings: linking population coding models to experimental data. Frontiers in Computational Neuroscience 5, 35, S. 1 - 2 (2011)

Macke, J.; Gerwinn, S.; White, L.; Kaschube, M.; Bethge, M.: Gaussian process methods for estimating cortical maps. NeuroImage 56 (2), S. 570 - 581 (2011)

Macke, J.; Opper, M.; Bethge, M.: Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity. Physical Review Letters 106 (20), 208102, S. 1 - 4 (2011)

Gerwinn, S.; Macke, J.; Bethge, M.: Reconstructing stimuli from the spike-times of leaky integrate and fire neurons. Frontiers in Neuroscience 5 (1), S. 1 - 16 (2011)

Lyamzin, D.; Macke, J.; Lesica, N.: Modeling population spike trains with specified time-varying spike rates, trial-to-trial variability, and pairwise signal and noise correlations. Frontiers in Computational Neuroscience 4, 144, S. 1 - 11 (2010)

Macke, J.; Wichmann, F.: Estimating predictive stimulus features from psychophysical data: The decision image technique applied to human faces. Journal of Vision 10 (5), 22, S. 1 - 24 (2010)

Gerwinn, S.; Macke, J.; Bethge, M.: Bayesian inference for generalized linear models for spiking neurons. Frontiers in Computational Neuroscience 4, 12, S. 1 - 17 (2010)

Gerwinn, S.; Macke, J.; Bethge, M.: Bayesian population decoding of spiking neurons. Frontiers in Computational Neuroscience 3, 21, S. 1 - 28 (2009)

Macke, J.; Berens, P.; Ecker, A.; Tolias, A.; Bethge, M.: Generating Spike Trains with Specified Correlation Coefficients. Neural computation 21 (2), S. 397 - 423 (2009)