@misc{10481/111632,
year = {2010},
month = {10},
url = {https://hdl.handle.net/10481/111632},
abstract = {This paper presents how a plausible cerebellum-like architecture can abstract corrective models in the framework of a robot control task when manipulating objects that significantly affect the dynamics of the system. The presented scheme is adequate to control non-stiff-joint robots with low-power actuators which involve controlling systems with high inertial components. We evaluate the way in which the cerebellum stores a model in the granule layer, how its microstructure can efficiently abstract models and deliver accurate corrective torques for increasing precision during object manipulation. Particularly we study how input sensory-motor representations can enhance model abstraction capabilities during accurate movements, making use of explicit (model-related input labels) and implicit model representations (sensory signals). Finally we focus on how our cerebellum model (using a temporal correlation kernel) properly deals with transmission delays in sensory-motor pathways.},
keywords = {Spiking neuron},
keywords = {Cerebellum},
keywords = {Adaptive},
keywords = {Simulation},
keywords = {Learning, Robot},
keywords = {Biological Control Systems},
title = {Cerebellar Spiking Engine: Towards Objet Model Abstraction in Manipulation},
doi = {10.1109/IJCNN.2010.5596531},
author = {Luque Sola, Niceto Rafael and Garrido Alcázar, Jesús Alberto and Carrillo Sánchez, Richard Rafael and Ros Vidal, Eduardo},
}