Cerebellum
Related papers: 20
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The cerebellum is a brain structure responsible for coordinating movement, motor learning, and sensory prediction. It functions as an internal forward model — continuously comparing predicted versus actual sensory consequences of movement and issuing corrective signals to refine motor output. In biological systems, it underlies smooth, adaptive motor control, timing precision, and procedural learning, with damage producing conditions like ataxia and impaired motor adaptation. In robotics and AI, the cerebellum serves as a powerful inspiration for adaptive control architectures. Researchers implement biologically grounded cerebellar models — often using spiking neural networks — to enable robots to learn and refine movement control in real time, compensating for unpredictable dynamics and actuator nonlinearities. These models have been applied to robot arm control, eye stabilization, mobile navigation, and rehabilitation devices, frequently combined with basal ganglia models for goal-directed behavior. The cerebellum matters to engineers because it offers a biologically validated solution to adaptive sensorimotor control: lightweight, fast-converging, and capable of generalization. Understanding and replicating its distributed plasticity mechanisms provides a principled path toward robots that learn movement skills with human-like efficiency and robustness.
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The cerebellum is involved in predicting the sensory consequences of action
Sarah‐Jayne Blakemore, Chris Frith, Daniel M. Wolpert
Citations: 597 • 2001
Short latency cerebellar modulation of the basal ganglia
Christopher H. Chen, Rachel Fremont, Eduardo E. Arteaga-Bracho, Kamran Khodakhah
Citations: 286 • 2014
Motor Learning and the Cerebellum
Chris I. De Zeeuw, Michiel M. ten Brinke
Citations: 259 • 2015
Behavioural and neural basis of anomalous motor learning in children with autism
Mollie K. Marko, Deana Crocetti, Thomas Hulst, Opher Donchin, Reza Shadmehr, Stewart H. Mostofsky
Citations: 164 • 2015
Long-term adaptation to dynamics of reaching movements: a PET study
Reza Nezafat, Reza Shadmehr, Henry H. Holcomb
Citations: 138 • 2001
A real-time spiking cerebellum model for learning robot control
Richard R. Carrillo, Eduardo Ros, Christian Boucheny, Olivier J. M. D. Coenen
Citations: 118 • 2008
Distributed Circuit Plasticity: New Clues for the Cerebellar Mechanisms of Learning
Egidio D’Angelo, Lisa Mapelli, Claudia Casellato, Jesús A. Garrido, Niceto R. Luque, Jessica Monaco, Francesca Prestori, Alessandra Pedrocchi, Eduardo Ros
Citations: 96 • 2015
Realtime cerebellum: A large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit
Tadashi Yamazaki, Jun Igarashi
Citations: 83 • 2013
Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network
Claudia Casellato, Alberto Antonietti, Jesús A. Garrido, Richard R. Carrillo, Niceto R. Luque, Eduardo Ros, Alessandra Pedrocchi, Egidio D’Angelo
Citations: 77 • 2014
Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation
Jesús A. Garrido, Niceto R. Luque, Egidio D’Angelo, Eduardo Ros
Citations: 76 • 2013
A mutation in Af4 is predicted to cause cerebellar ataxia and cataracts in the robotic mouse.
Adrian M. Isaacs, Peter L. Oliver, Emma Jones, Alexander Jeans, Allyson C. Potter, Berit H. Hovik, Patrick M. Nolan, Lucie Vizor, P. H. Glenister, Anna Katharina Simon, Ian C. Gray, Nigel K. Spurr, A. Jackie Hunter, Kay E. Davies
Citations: 72 • 2003
Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks
Claudia Casellato, Alberto Antonietti, Jesús A. Garrido, Giancarlo Ferrigno, Egidio D’Angelo, Alessandra Pedrocchi
Citations: 71 • 2015
The cerebellum in action: a simulation and robotics study
Constanze Hofstötter, Matti Mintz, Paul F. M. J. Verschure
Citations: 70 • 2002
How does brain activation differ in children with unilateral cerebral palsy compared to typically developing children, during active and passive movements, and tactile stimulation? An fMRI study
Ann Van de Winckel, Katrijn Klingels, Frans Bruyninckx, Nici Wenderoth, Ronald Peeters, Stefan Sunaert, Wim Van Hecke, Paul De Cock, Maria Eyssen, Willy De Weerdt, Hilde Feys
Citations: 69 • 2012
A cerebellar model for predictive motor control tested in a brain-based device
Jeffrey L. McKinstry, Gerald M. Edelman, Jeffrey L. Krichmar
Citations: 67 • 2006
Connectivity alterations assessed by combining fMRI and MR-compatible hand robots in chronic stroke
Dionyssios Mintzopoulos, Loukas G. Astrakas, Azadeh Khanicheh, Angelos A. Konstas, Aneesh B. Singhal, Michael A. Moskowitz, Bruce R. Rosen, A. Aria Tzika
Citations: 65 • 2009
Realistic modeling of neurons and networks: towards brain simulation.
Egidio D’Angelo, Sergio Solinas, Jesús A. Garrido, Claudia Casellato, Alessandra Pedrocchi, Jonathan Mapelli, Daniela Gandolfi, Francesca Prestori
Citations: 64 • 2014
Cerebellar-Inspired Adaptive Control of a Robot Eye Actuated by Pneumatic Artificial Muscles
Alexander Lenz, Sean Anderson, Tony Pipe, Chris Melhuish, Paul Dean, John Porrill
Citations: 61 • 2009
Mediation of Af4 protein function in the cerebellum by Siah proteins
Peter L. Oliver, Emmanuelle Bitoun, Joanne Clark, Emma Jones, Kay E. Davies
Citations: 53 • 2004
Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation
Niceto R. Luque, Jesús A. Garrido, Richard R. Carrillo, Egidio D’Angelo, Eduardo Ros
Citations: 48 • 2014