Computational models of the brain provide a unique opportunity to study the brain and to contribute to progress in personalized and precision medicine for brain diseases. Despite their clear utility, however, they raise some ethical, philosophical, social, and cultural questions. Identifying and examining them is key.
One of the first challenges that the identification and examination of...
Spike-based neuromorphic computing realizes an in-memory, event-based computing paradigm. By transferring results and ideas from Neuroscience to technology, it allows us to overcome the power wall our CPU-centric CMOS technology is facing.
This talk will present an analog hardware realization of spike-based neuromorphic computing developed at Heidelberg University: The BrainScaleS system.
It...
Modeling successful online learning in real-world environments is an aspirational goal of neuroscience and artificial intelligence technologies. Using deep learning for modeling synaptic plasticity in the brain has led to recent breakthroughs, but standard deep neural networks struggle to achieve real-world, online learning. Furthermore, the randomized, energy- and data-intensive process for...
