Keynotes

Neuromorphic AI for small, autonomous drones

Spurred by the quick progress in artificial intelligence, autonomous robots promise to revolutionize our economy and society. However, current-day AI requires excessive computational effort, leading to extra robot weight, costs, and energy consumption. This makes robots with AI large, heavy, and dangerous, which negatively affects their societal acceptance. To realize a future of competent and safe autonomous robots, AI should become orders-of-magnitude more efficient.

Neuromorphic sensing and processing promises substantially faster and more energy efficient robotic AI. In order to show its potential, in my lab we tackle the daunting challenge of developing a neuromorphic AI that provides full autonomy to tiny, severely resource-restricted drones. I will present our ongoing work on realizing capabilities such as ego-motion estimation and control purely with SNNs, and share our findings on the actual latency and power usage of existing neuromorphic chips. Finally, I will present Bee-Nav, our honeybee-inspired approach to navigation with tiny, efficient neural networks.

From Sound to Meaning: What Human Auditory Cortex Can Teach Neuromorphic Computing

Human hearing transforms continuously changing acoustic signals into meaningful information about the world: who is speaking, what is happening, and which events matter. How does the brain accomplish this transformation, and which computational principles could inform neuromorphic listening systems? 

In this talk, I will present our research combining ultra-high-field functional MRI, perceptual experiments and NeuroAI modeling to investigate how the human auditory cortex represents natural sounds. I will show how this approach reveals sound processing at multiple spectral and temporal scales, and intermediate representations that bridge acoustic features and semantic interpretation. Comparisons with deep neural networks help identify which representations best explain brain responses and human perception. 

Building on these findings, I will outline opportunities for neuromorphic auditory computing, including architectures that integrate information across timescales and learning objectives that capture perceptually meaningful sound structure. I will also discuss how neural and behavioural measurements could provide benchmarks for evaluating such systems. The broader aim is to translate insights into human hearing into testable principles for efficient, adaptive artificial listening.