Cerebellar Spiking Engine: Towards Objet Model Abstraction in Manipulation Luque Sola, Niceto Rafael Garrido Alcázar, Jesús Alberto Carrillo Sánchez, Richard Rafael Ros Vidal, Eduardo Spiking neuron Cerebellum Adaptive Simulation Learning, Robot Biological Control Systems 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. 2026-02-27T07:46:36Z 2026-02-27T07:46:36Z 2010-10-14 conference output Luque, N. R., Garrido, J. A., Carrillo, R. R., & Ros, E. (2010, July). Cerebellar spiking engine: Towards objet model abstraction in manipulation. In The 2010 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. https://hdl.handle.net/10481/111632 10.1109/IJCNN.2010.5596531 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional