Claudia Casellato
Papers
16
Total Citations
495
H-Index
10
About
Claudia Casellato is a leading researcher in neurorobotics and computational neuroscience, whose work bridges the gap between brain-inspired algorithms and real-world robotic control. Her primary research areas include cerebellar modeling, spiking neural networks, and adaptive sensorimotor control. Casellato’s major contributions lie in demonstrating how distributed cerebellar plasticity can implement generalized, multiple-scale memory components for real-robot tasks, as shown in her highly cited 2015 paper (71 citations). She has developed versatile spiking cerebellar networks that drive adaptive robotic control (77 citations), and has explored how distributed circuit plasticity provides new insights into cerebellar learning mechanisms (96 citations). Her work extends to clinical applications, including error-enhancing robot therapy for motor control improvement in childhood dystonia, and socially assistive robotics for autistic children using the NAO humanoid robot. With over 465 citations across her top ten papers, Casellato has established herself as a pioneer in brain-inspired robotics, creating functional links between neural plasticity mechanisms, circuit dynamics, and behavioral learning in both simulated and physical robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network77 citations · 2014
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- 4Realistic modeling of neurons and networks: towards brain simulation.64 citations · 2014
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