Papers
5
Total Citations
199
H-Index
4
About
Matthias Ziegler’s research spans the frontiers of neuroscience, human performance, and computational imaging, with a unifying focus on variability—whether in spinal learning, cognitive workload, or light-field reconstruction. His most influential work, “Why Variability Facilitates Spinal Learning” (92 citations), demonstrated that introducing variability into training enhances motor recovery in spinal cord-injured rats, a finding with profound implications for rehabilitation. He further advanced neural repair with “Further evidence of olfactory ensheathing glia facilitating axonal regeneration after a complete spinal cord transection” (69 citations), showing how glial cells promote axonal regrowth. More recently, Ziegler has tackled human-machine systems, co-authoring “Mental workload assessment by monitoring brain, heart, and eye with six biomedical modalities during six cognitive tasks” (31 citations), which integrates multimodal biosignals for real-time workload estimation—critical for aviation and robotic surgery. His work on non-planar light-field datasets (2019) and AI-driven performance augmentation (2018) underscores his versatility, bridging biology and engineering. With over 200 total citations, Ziegler’s contributions illuminate how variability, whether in neural circuits or cognitive states, can be harnessed to improve learning, recovery, and human-machine interaction.
Research Focus
Key Achievements
Top Papers
- 1Why Variability Facilitates Spinal Learning92 citations · 2010
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