L. Theis, S. Gerwinn, F. Sinz, and M. Bethge
In All Likelihood, Deep Belief Is Not Enough
Journal of Machine Learning Research, 12, 3071-3096, 2011
#natural image statistics, #deep belief networks, #boltzmann machines, #deep learning
Code, PDF, BibTex
A. S. Ecker, P. Berens, A. S. Tolias, and M. Bethge
The effect of noise correlations in populations of diversely tuned neurons
The Journal of Neuroscience, 31(40), 14272-14283, 2011
#noise correlations, #population coding, #fisher information, #orientation
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J. H. Macke, M. Opper, and M. Bethge
Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity
Physical Review Letters, 106(20), 2011
#population coding
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P. Berens, A. S. Ecker, S. Gerwinn, A. S. Tolias, and M. Bethge
Reassessing optimal neural population codes with neurometric functions
Proceedings of the National Academy of Sciences of the United States of America, 108(11), 4423-4428, 2011
#fisher information, #population coding, #mean squared error, #discrimination error, #neurometric function
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J. Macke, P. Berens, and M. Bethge
Statistical analysis of multi-cell recordings: linking population coding models to experimental data
Frontiers in Computational Neuroscience, 5(35), 2011
#multi-cell recordings, #population models, #statistical analysis
URL, PDF, BibTex
T. Kitching, A. Amara, M. Gill, S. Harmeling, C. Heymans, R. Massey, B. Rowe, T. Schrabback, et al.
Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook
The Annals of Applied Statistics, 5(3), 2231-2263, 2011
URL, BibTex
S. Gerwinn, J. Macke, and M. Bethge
Reconstructing stimuli from the spike times of leaky integrate and fire neurons
Frontiers in Neuroscience, 5, 2011
#population coding, #decoding, #bayesian inference, #spiking neurons
University of Tuebingen BCCN CIN MPI