Lecture (Other) FZJ-2025-01617

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png
Deep Learning for Neuroscience

 ;  ;  ;  ;

2024

Lecture at Forschungszentrum Jülich (Jülich, Germany), 19 Nov 2024 - 19 Nov 20242024-11-192024-11-19

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.


Contributing Institute(s):
  1. Jülich Supercomputing Center (JSC)
  2. Strukturelle und funktionelle Organisation des Gehirns (INM-1)
Research Program(s):
  1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
  2. 5251 - Multilevel Brain Organization and Variability (POF4-525) (POF4-525)
  3. Helmholtz AI Consultant Team FB Information (E54.303.11) (E54.303.11)

Appears in the scientific report 2024
Click to display QR Code for this record

The record appears in these collections:
Document types > Presentations > Lectures
Institute Collections > INM > INM-1
Workflow collections > Public records
Institute Collections > JSC
Publications database

 Record created 2025-01-31, last modified 2025-02-03


External link:
Download fulltext
Fulltext
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)