bioRxiv preprint doi: https://doi.org/10.1101/2020.10.05.326256; this version posted April 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Published as a conference paper at ICLR 2021 GENERALIZATION IN DATA-DRIVEN MODELS OF PRIMARY VISUAL CORTEX Konstantin-Klemens Lurz,1-2,* Mohammad Bashiri,1-2 Konstantin Willeke,1-2 Akshay K. Jagadish,1 Eric Wang,4-5 Edgar Y. Walker,1,2,4-5 Santiago A. Cadena,2,3 Taliah Muhammad,4-5 Erick Cobos,4-5 Andreas S. Tolias,4-5 Alexander S. Ecker,6 Fabian H. Sinz1-5, ** 1 Institute for Bioinformatics and Medical Informatics, University of Tübingen, Germany 2 International Max Planck Research School for Intelligent Systems, Tübingen, Germany 3 Bernstein Center for Computational Neuroscience, University of Tübingen, Germany 4 Department for Neuroscience, Baylor College of Medicine, Houston, TX, USA 5 Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA 6 Department of Computer Science / Campus Institute Data Science, University of Göttingen, Germany *
[email protected], **
[email protected] ABSTRACT Deep neural networks (DNN) have set new standards at predicting responses of neural populations to visual input. Most such DNNs consist of a convolutional network (core) shared across all neurons which learns a representation of neural computation in visual cortex and a neuron-specific readout that linearly combines the relevant features in this representation.