Papers
6
Total Citations
67
H-Index
5
About
Philipp Stratmann is a leading researcher at the intersection of computational neuroscience, neuromorphic engineering, and robotic control. His work focuses on understanding how biological systems—particularly the nervous system—achieve energy-efficient, highly dynamic movement, and then translating those principles into neuromorphic hardware and control algorithms. Stratmann’s major contributions include pioneering the use of Principal Component Analysis (PCA) to model how the nervous system tunes muscle output for energy-efficient periodic motion, and developing reflex- and Central Pattern Generator (CPG)-based control strategies for compliant legged robots. His research on neuromorphic quadratic programming and state-space models on Intel’s Loihi 2 chip has advanced real-time, low-energy model predictive control for size-, weight-, and power-constrained (SWaP) autonomous systems. With over 67 citations across his most-cited works, Stratmann’s impact is evident in both robotics and neuromorphic computing communities. Notably, his 2024 paper on neuromorphic quadratic programming and his 2025 work on diagonal state-space models for streaming sequence processing represent cutting-edge efforts to bridge deep learning efficiency with novel hardware architectures. Stratmann’s work is essential reading for anyone interested in bio-inspired control, energy-efficient robotics, or neuromorphic computing.
Research Focus
Key Achievements
Top Papers
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- 2Scaling Our World View: How Monoamines Can Put Context Into Brain Circuitry16 citations · 2018
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