www-from-model/content/publications/e3acf3af-3b8b-40ea-87dc-18d29446ced8/_index.md

title	persons	topics	params
Brains on beats	michael-hanke	predictive-data-analysis naturalistic-neuroimaging	graphRootNodePID pid doi date title description kind author topic xyzrins:publications/e3acf3af-3b8b-40ea-87dc-18d29446ced8 xyzrins:publications/e3acf3af-3b8b-40ea-87dc-18d29446ced8 null null Brains on beats We developed task-optimized deep neural networks (DNNs) that achieved state-of-the-art performance in different evaluation scenarios for automatic music tagging. These DNNs were subsequently used to probe the neural representations of music. Representational similarity analysis revealed the existence of a representational gradient across the superior temporal gyrus (STG). Anterior STG was shown to be more sensitive to low-level stimulus features encoded in shallow DNN layers whereas posterior STG was shown to be more sensitive to high-level stimulus features encoded in deep DNN layers. bibo:AcademicArticle pid given_name family_name xyzrins:persons/michael-hanke Michael Hanke pid display_label xyzrins:topics/predictive-data-analysis Predictive data analysis pid display_label xyzrins:topics/naturalistic-neuroimaging Naturalistic neuroimaging