Papers
9
Total Citations
305
H-Index
6
About
Alberto Antonietti is a computational neuroscientist whose work bridges theoretical neuroscience, brain-inspired robotics, and human-robot interaction. His research centers on two distinct but complementary areas: cerebellar computational modeling and the psychological dimensions of robot perception. Antonietti has made significant contributions to understanding the cerebellum's role in motor learning and sensorimotor control. His development of biologically realistic spiking neural network models of the cerebellum, successfully deployed to control real robots including the humanoid NAO platform, has advanced the field of neurorobotics considerably. His 2014 paper on adaptive robotic control driven by cerebellar networks has accumulated 77 citations, while his work on distributed cerebellar plasticity for multi-scale motor memory garnered 71 citations, reflecting the sustained influence of these contributions on both neuroscience and robotics communities. Beyond motor control, Antonietti has explored the "Uncanny Valley" phenomenon in human-robot interaction, contributing to a notable multisite study — his most cited work with 90 citations — examining perceptual category confusion as a potential mechanism underlying human aversion to near-human robots. His more recent work on whisker-inspired sensorimotor systems and cellular-to-architecture linking frameworks signals a continued commitment to biologically grounded approaches that connect microscale neural dynamics to emergent behavior.
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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