AI methods for building ultrahigh resolution human brain models (INM-1)
Understanding cognition through AI
Facing the challenges of brain complexity and big data analytics, research at INM-1 addresses human brain organization on its different scales, hand in hand with brain simulation and the investigation of learning in artificial and biological neural networks to advance our understanding of cognition, which opens perspectives to discover novel mechanisms for AI.
The research group at INM-1 will be linked to the Big Data Analytics group headed by Timo Dickscheid, which develops methods for high throughput analysis and 3D reconstruction of microscopic image data, and neuroinformatics solutions for managing high-resolution brain atlases. The focus of the young investigator group will be on deep learning methods for microstructural object detection and classification with limited training data, supported by shape and topological constraints. Driven by continuously increasing image resolutions and data volumes in high-throughput settings, methods are designed for distributed operation on HPC systems.