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Ahmadi, K., Pouretemad, H. R., Esfandiari, J., Yoonessi, A., & Yoonessi, A. (2015). Psychophysical evidence for impaired Magno, Parvo, and Konio-cellular pathways in dyslexic childrenJournal of ophthalmic & vision research10(4), 433.

Hoffmann, M. B., & Dumoulin, S. O. (2015). Congenital visual pathway abnormalities: a window onto cortical stability and plasticityTrends in neurosciences38(1), 55-65.

Hoffmann MB, Thieme H, Ahmadi K (2016). Potential von fMRT für die Funktionsüberprüfung des pathologischen Sehsystems. Klinische Monatsblätter für Augenheilkunde (accepted)

Moutsiana, C., de Haas, B., Papageorgiou, A., van Dijk, J. A., Balraj, A., Greenwood, J. A., & Schwarzkopf, D. S. (2016). Cortical idiosyncrasies predict the perception of object size. Nature Communications, 7, 12110.

Raz, N., & Levin, N. (2016). Neuro-visual rehabilitationJournal of Neurology, 1-8.

Semeniuta, S., Severyn, A., & Barth, E. (2016). Recurrent dropout without memory lossarXiv preprint arXiv:1603.05118.

Semeniuta, S. & Barth, E. (2016). Image Classification with Recurrent Attention Models. IEEE SSCI 16

van Dijk, J. A., de Haas, B., Moutsiana, C., & Schwarzkopf, D. S. (2016). Intersession reliability of population receptive field estimates. NeuroImage, 143, 293-303.

Ahmadi, K., Fracasso, A., van Dijk, J.A., Kruijt, C., van Genderen, M., Dumoulin, S.O., & Hoffmann, M.N. (2017). Altered organization of the visual cortex in FHONDA syndrome. NeuroImage, 1-8.

Mikellidou, K., Kurzawski, J.W., Frijia, F., Montanaro, D., Greco, V., Burr, D.C., & Morrone, M.C. (2017). Area Prostriata in the Human BrainCurrent Biology, 19, 3056-3060

Binda, P., Kurzawski, J.W., Lunghi, C., Biagi, L., Tosetti, M, Morrone, M.C. (2018). Response to short-term deprivation of the human adult visual cortex measured with 7T BOLDeLife.40014

Bhat, A., Cicchini, G.M., & Burr, D.C. Inhibitory surrounds of motion mechanisms revealed by continuous tracking. (2018). Journal of Vision, 18, 1-10

de Best, P.B., Raz, N., Dumoulin, S.O., & Levin, N. (2018). How ocular dominance and binocularity are reflected by the population receptive field properties. IOVS, 56, 5301-5311

Klein, B.P., Fracasso, A., van Dijk, J.A., Paffen, C.L.E., te Pas, S.F., & Dumoulin, S.O. Cortical depth dependent population receptive field attraction by spatial attention in human V1. (2018). NeuroImage, 176, 301-312

Semeniuta, S., Severyn, A., & Barth, E. (2017). A hybrid variational autoencoder for text generation. Conference on Emperical Methods in Natural Language Processing (proceedings), 627-637

Semeniuta, S., Severyn, A., & Gelly, S. (2018). On accurate evaluation of GAN for language generation. arXiv preprint arXiv:1806.04936v2

Grillini, A., Ombelet, D., Soans, R.S., & Cornelissen, F.W. (2018). Toward using the spatio-temporal properties of eye movements to classify visual field defects. ETRA ’18 Proceedings, 38

Yildrim, F., Carvalho, J., Cornelissen, & F.W. (2018). A second-order orientation-contrast stimulus for population-receptive-field-based retinotopic mappingNeuroImage, 164, 183-193

Himmelberg, M.M., West, R.J.H., Elliot, C.J.H., & Wade, A.R. (2018). Abnormal visual gain control and excitotoxicity in early-onset Parkinson’s disease Drosophila modelsJournal of Neurophysiology, 119, 957-970

Himmelberg, M.M, West, R.J.H., West, Wade, A.R.W., & Elliot, C.J.H. (2018). A perspective plus in Parkinson’s diseaseMovement Disorders, 33, 248

Himmelberg, M.M., & Wade, A.R. (2019) Eccentricity-dependent temporal contrast tuning in human visual cortex measured with fMRINeuroImage, 184, 462-474

Hernández-García, A., & König, P. (2018). Further advantages of data augmentation on convolution neural networks. Artificial Neural Networks and Machine Learning (conference proceedings), 95-103

Hernández-García, A. (2017). Perceived emotion from images through deep neural networks. Seventh International Conference on Affective Computing and Intelligent Interaction (conference proceedings), 566-570

Grillini, A., Renken, R.J., & Cornelissen, F.W. (2019) Attention modulation of visual spatial integration: psychophysical evidence supported by population coding modeling. Journal of Cognitive Neuroscience, 1-14

Hernández-García, A., & König, P. (2018). Do deep nets really need weight decay and dropout? arXiv preprint arXiv:1802.07042