Brain inspiration in neuromorphic computing


Emre Neftci | Forschungszentrum Jülich and RWTH Aachen, Germany
Tobias Gemmeke | RWTH Aachen, Germany


Neuromorphic computing has gained significant traction in the last decade, encompassing a diverse array of research areas ranging from artificial neural networks to reproductions of biological neural networks. The importance of understanding the principles of brain computation and incorporating them into artificial systems, such as conventional computers or dedicated neuromorphic hardware, is often thought to be critical to advance AI technologies. However, with the advent of powerful vision and language neural network models, the necessity and degree of brain inspiration to achieve intelligence has become a subject of debate. Despite this, the human brain consumes far less energy for similar tasks as current AI while demonstrating greater resilience to ambiguous cues and physical damage. This raises important questions: In which tasks and metrics do brain-inspired, embodied, or physics-based computing outperform conventional computers and theories? Can brain-inspired solutions enhance state-of-the-art artificial neural networks, or do they require fundamentally different architectures? This workshop aims to stimulate an interdisciplinary discussion on the significance of neuroscience-inspired approaches in developing novel computing paradigms.

Schedule (CEST)

Tuesday, Sept 26


Herbert Jaeger | University of Groningen, The Netherlands


Charlotte Frenkel  | Delft University of Technology, The Netherlands 
Merging insights from artificial and biological neural networks – A competitive advantage for neuromorphic edge


30 min coffee break


Christian Tetzlaff | University Medical Center Göttingen, Germany
Using neuromorphic computing to investigate synaptic plasticity


Guillaume Bellec | Swiss Federal Institute of Technology, Lausanne,Switzerland
Spiking compression: low bit-rate audio compression with event-based autoencoders


End of first day

Wednesday, Sept 27


Johannes SchemmelHeidelberg University, Germany
Analog circuits for brain emulation – is this still a viable approach?


Jenia Jitsev | Forschungszentrum Jülich, Germany

Scalable self-supervised learning and generalization: non-brain and brain inspired perspectives


30 min coffee break


Holger Rauhut | Aachen University, Germany


Melika Payvand | University of Zurich, Switzerland
What can the structure of brain circuitry at different spatial scales tell us about designing efficient AI hardware?