% IMPORTANT: The following is UTF-8 encoded. This means that in the presence % of non-ASCII characters, it will not work with BibTeX 0.99 or older. % Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or % “biber”. @MISC{Schiffer:1033605, author = {Schiffer, Christian and Dickscheid, Timo and Benassou, Sabrina and Kasravi, Javad and Strube, Alexandre}, title = {{T}utorial: {D}eep {L}earning for {N}euroscience}, reportid = {FZJ-2024-06485}, year = {2024}, abstract = {Machine Learning – in particular deep learning – has become an indispensable tool for analyzing large neuroscience datasets. The Helmholtz AI team at Jülich is closely connected to these developments and supports research activities at the intersection of AI, high-performance computing (HPC) and neuroscience. Many of the methods and solutions are not limited to neuroscience and medical applications, but can be transferred to different tasks and scientific domains.This tutorial we will give an overview of state-of-the-art deep learning methods in the context of biomedical image analysis and show concrete examples in INM where deep learning already supports neuroscientists in analyzing their data. The second part of this tutorial will offer a hands-on course on how to bring deep learning pipelines on JSC’s HPC systems.}, month = {Nov}, date = {2024-11-19}, organization = {INM Retreat 2024, Jülich (Germany), 19 Nov 2024 - 19 Nov 2024}, subtyp = {Outreach}, cin = {INM-1}, cid = {I:(DE-Juel1)INM-1-20090406}, pnm = {5254 - Neuroscientific Data Analytics and AI (POF4-525) / Helmholtz AI - Helmholtz Artificial Intelligence Coordination Unit – Local Unit FZJ (E.40401.62) / HIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015) / X-BRAIN (ZT-I-PF-4-061)}, pid = {G:(DE-HGF)POF4-5254 / G:(DE-Juel-1)E.40401.62 / G:(DE-HGF)InterLabs-0015 / G:(DE-HGF)ZT-I-PF-4-061}, typ = {PUB:(DE-HGF)17}, url = {https://juser.fz-juelich.de/record/1033605}, }