Klaus Obermayer
Papers
4
Total Citations
93
H-Index
4
About
Klaus Obermayer is a leading figure in computational neuroscience and machine learning, with a research portfolio that bridges autonomous learning, reinforcement learning (RL), and auditory perception. His work is distinguished by a focus on how agents—biological or artificial—can efficiently represent and interact with complex, dynamic environments. A key contribution is his pioneering exploration of autonomously learning state representations for RL agents from real-world sensor data, a concept that has garnered 51 citations and is foundational for developing more adaptive AI systems. He has also advanced the theoretical underpinnings of RL by investigating how to construct optimal approximation spaces for value functions, as seen in his 2013 work (22 citations), which directly impacts the efficiency of algorithms like LSTD. Beyond RL, Obermayer has made notable strides in robust sound detection within binaural auditory scenes (14 citations), addressing the challenge of identifying environmental sounds amidst multiple distractors. Most recently, his 2025 robotics-inspired scanpath model (6 citations) integrates uncertainty and semantic object cues to explain gaze guidance in dynamic scenes, offering a novel, unified perspective on perception and attention. His work consistently demonstrates a commitment to solving real-world perception and control problems, making him a key thinker for students and researchers interested in the intersection of neuroscience, robotics, and AI.
Research Focus
Key Achievements
Top Papers
- 1
- 2Construction of approximation spaces for reinforcement learning22 citations · 2013
- 3Robust Detection of Environmental Sounds in Binaural Auditory Scenes14 citations · 2017
- 4