Brain inspiration in neuromorphic computing
Organizers
Emre Neftci | Forschungszentrum Jülich and RWTH Aachen, Germany
Tobias Gemmeke | RWTH Aachen, Germany
Abstract
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.
