Neural data analysis, modeling, and tools

  • The functional diversity of retinal ganglion cells in the mouse (T Baden, P Berens, K Franke, M Román Rosón, M Bethge, T Euler)
  • Deep convolutional models improve predictions of macaque V1 responses to natural images (SA Cadena, GH Denfield, EY Walker, LA Gatys, AS Tolias, M Bethge, …)
  • Decorrelated neuronal firing in cortical microcircuits (AS Ecker, P Berens, GA Keliris, M Bethge, NK Logothetis, AS Tolias)
  • Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq (CR Cadwell, A Palasantza, X Jiang, P Berens, Q Deng, M Yilmaz, …)
  • State dependence of noise correlations in macaque primary visual cortex (AS Ecker, P Berens, RJ Cotton, M Subramaniyan, GH Denfield, …)
  • The effect of noise correlations in populations of diversely tuned neurons (A Ecker, P Berens, A Tolias, M Bethge)
  • Neural system identification for large populations separating what and where (DA Klindt, AS Ecker, T Euler, M Bethge)
  • Optimal short-term population coding: when Fisher information fails (M Bethge, D Rotermund, K Pawelzik)
  • Inhibition decorrelates visual feature representations in the inner retina (K Franke, P Berens, T Schubert, M Bethge, T Euler, T Baden)
  • Population code in mouse V1 facilitates readout of natural scenes through increased sparseness (E Froudarakis, P Berens, AS Ecker, RJ Cotton, FH Sinz, D Yatsenko, …)
  • Inferring decoding strategies from choice probabilities in the presence of correlated variability (RM Haefner, S Gerwinn, JH Macke, M Bethge)
  • Dynamics of population rate codes in ensembles of neocortical neurons (G Silberberg, M Bethge, H Markram, K Pawelzik, M Tsodyks)
  • Spikes in mammalian bipolar cells support temporal layering of the inner retina (T Baden, P Berens, M Bethge, T Euler)
  • A fast and simple population code for orientation in primate V1 (P Berens, AS Ecker, RJ Cotton, WJ Ma, M Bethge, AS Tolias)
  • Common input explains higher-order correlations and entropy in a simple model of neural population activity (JH Macke, M Opper, M Bethge)
  • Reassessing optimal neural population codes with neurometric functions (P Berens, AS Ecker, S Gerwinn, AS Tolias, M Bethge)
  • A rotation-equivariant convolutional neural network model of primary visual cortex (AS Ecker, FH Sinz, E Froudarakis, PG Fahey, SA Cadena, EY Walker, …)
  • Reading out olfactory receptors: feedforward circuits detect odors in mixtures without demixing (A Mathis, D Rokni, V Kapoor, M Bethge, VN Murthy)
  • Digital twin reveals combinatorial code of non-linear computations in the mouse primary visual cortex (I Ustyuzhaninov, MF Burg, SA Cadena, J Fu, T Muhammad, K Ponder, …)
Matthias Bethge
Matthias Bethge
Professor for Computational Neuroscience and Machine Learning & Director of the Tübingen AI Center

Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy.