Paul Dean
Papers
12
Total Citations
333
H-Index
10
About
Paul Dean is a computational neuroscientist whose research sits at the fascinating intersection of cerebellar biology, adaptive control theory, and robotics. Over several decades, he has made foundational contributions to understanding how the cerebellum functions as an adaptive filter — a mechanism that decorrelates motor commands from their sensory consequences to enable precise, flexible movement control. His adaptive filter model of cerebellar microcircuitry has proven remarkably versatile, informing both our understanding of biological systems such as the vestibulo-ocular reflex and the design of bio-inspired robotic controllers. Dean's most-cited work (2006, 66 citations) demonstrated how recurrent cerebellar loops simplify control of redundant and nonlinear motor systems, while subsequent research applied these principles to robot eyes driven by pneumatic artificial muscles and to whisking robots capable of canceling self-generated sensory noise. His 2016 work extended these ideas to cutting-edge dielectric elastomer actuators for soft robotics. With contributions spanning neural network architectures, novelty detection, biohybrid control systems, and multisensory calibration in biomimetic robots, Dean's research has profoundly shaped how engineers and neuroscientists alike think about adaptive, brain-inspired control.
Research Focus
Key Achievements
Top Papers
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- 3Adaptive Cancelation of Self-Generated Sensory Signals in a Whisking Robot41 citations · 2010
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- 10Visual-tactile sensory map calibration of a biomimetic whiskered robot14 citations · 2016