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About

Benedikt Feldotto is a leading researcher in neurorobotics and embodied intelligence, whose work bridges computational neuroscience, robotics, and high-performance computing. His primary research areas include brain-inspired control strategies for dynamic locomotion, spiking neural network simulation, and the development of digital brain-body models for embodied learning. Feldotto’s major contributions include pioneering the use of reflex- and Central Pattern Generator (CPG)-based control strategies for highly-dynamic compliant movements, such as running and hopping, as demonstrated in his most-cited work (11 citations). He also played a key role in developing the Neurorobotics Platform (NRP) within the Human Brain Project, enabling researchers to connect spiking neural networks to virtual and real robots for embodiment experiments. Notably, he led groundbreaking distributed simulations of a spiking cortico-basal ganglia-cerebellar-thalamic brain model coupled with a realistic mouse musculoskeletal body, spanning multiple computers including the supercomputer Fugaku (7 citations). His work on Hebbian learning and Hierarchical Temporal Memory (HTM) for online prediction and classical conditioning in anthropomimetic robots further showcases his impact. With over 40 citations across his top papers, Feldotto’s research is foundational for advancing autonomous, adaptive robotic systems and understanding biological motion learning.