_{Publications:
Computational Vision and Neuroscience} 

Gatys, L.; Ecker, A.; Bethge, M.: Texture and art with deep neural networks. Current Opinion in Neurobiology 46, pp. 178 - 186 (2017)

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), pp. 1 - 23 (2017)

Wallis, T.; Funke, C.; Ecker, A.; Gatys, L.; Wichmann, F.; Bethge, M.: A parametric texture model based on deep convolutional features closely matches texture appearance for humans. Journal of Vision 17 (12), 5, pp. 1 - 29 (2017)

Wallis, T.; Tobias, S.; Bethge, M.; Wichmann, F.: Detecting distortions of peripherally presented letter stimuli under crowded conditions. Attention, Perception, Psychophysics 79 (3), pp. 850 - 862 (2017)

Franke, K.; Berens, P.; Schubert, T.; Bethge, M.; Euler, T.; Baden, T.: Inhibition decorrelates visual feature representations in the inner retina. Nature 542 (7642), pp. 439 - 444 (2017)

Bethge, M.: Wie Maschinen lernen: Deep Learning. HumanIThesia-Kongress „Ethik und KI“ 2017, Tübingen, Germany (2017)

Bethge, M.: When will signatures of criticality provide critical insights about neural computations? Excellence Workshop "Dynamical Network States, Criticality and Cortical Function", Delmenhorst, Germany (2017)

Klindt, D.; Ecker, A.; Euler, T.; Bethge, M.: Neural system identification for large populations separating “what” and “where”. Bernstein Conference 2017, Berlin, Germany (2017)

Vinogradov, O.; Ecker, A.; Denfield, G.; Tolias, A.; Bethge, M.: Mixed latent variable model of attention in V1. Bernstein Conference 2017, Berlin, Germany (2017)

Denfield, G.; Ecker, A.; Tolias, A.: The Role of Internal Signals in Structuring V1 Population Activity. 27th Annual Rush and Helen Record Neuroscience Forum, Galveston, TX, USA (2017)