AUTHOR=Celeghin Alessia , Borriero Alessio , Orsenigo Davide , Diano Matteo , Méndez Guerrero Carlos Andrés , Perotti Alan , Petri Giovanni , Tamietto Marco TITLE=Convolutional neural networks for vision neuroscience: significance, developments, and outstanding issues JOURNAL=Frontiers in Computational Neuroscience VOLUME=17 YEAR=2023 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2023.1153572 DOI=10.3389/fncom.2023.1153572 ISSN=1662-5188 ABSTRACT=
Convolutional Neural Networks (CNN) are a class of machine learning models predominately used in computer vision tasks and can achieve human-like performance through learning from experience. Their striking similarities to the structural and functional principles of the primate visual system allow for comparisons between these artificial networks and their biological counterparts, enabling exploration of how visual functions and neural representations may emerge in the real brain from a limited set of computational principles. After considering the basic features of CNNs, we discuss the opportunities and challenges of endorsing CNNs as