Richard R. Carrillo
Papers
12
Total Citations
570
H-Index
10
About
Richard R. Carrillo is a leading researcher at the intersection of computational neuroscience and robotics, specializing in spiking neural networks and cerebellar-inspired control systems. His groundbreaking work focuses on developing biologically-plausible models of the cerebellum to solve fundamental challenges in robotic control, particularly in adaptive motor learning and real-time performance. Carrillo's most influential contribution is the "Real-Time Computing Platform for Spiking Neurons (RT-Spike)" (78 citations), a hybrid hardware-software system that enables the simulation of arbitrary spiking neural networks in real time. His seminal paper "A real-time spiking cerebellum model for learning robot control" (118 citations) demonstrates how cerebellar architectures can drive adaptive robotic behavior. Carrillo has made significant advances in addressing the nondeterministic time delay problem in human-robot interaction (55 citations), and his work on adaptive cerebellar spiking models (74 citations) shows how context-switching and noise robustness can be achieved in robotic control loops. His research has accumulated over 560 citations, establishing him as a key figure in neurorobotics who bridges the gap between neural computation and practical robotic applications.
Research Focus
Key Achievements
Top Papers
- 1A real-time spiking cerebellum model for learning robot control118 citations · 2008
- 2Real-Time Computing Platform for Spiking Neurons (RT-Spike)78 citations · 2006
- 3Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network77 citations · 2014
- 4
- 5
- 6
- 7Cerebellarlike Corrective Model Inference Engine for Manipulation Tasks44 citations · 2011
- 8
- 9Hardware event-driven simulation engine for spiking neural networks14 citations · 2007
- 10