Adaptive Cerebellar Spiking Model Embedded in the Control Loop: Context Switching and Robustness Against Noise Luque Sola, Niceto Rafael Garrido Alcázar, Jesús Alberto Carrillo Sánchez, Richard Rafael Tolu, Silvia Ros Vidal, Eduardo Cerebellum STDP robot simulation learning biological control system noise This work evaluates the capability of a spiking cerebellar model embedded in different loop architectures (recurrent, forward, and forward&recurrent) to control a robotic arm (three degrees of freedom) using a biologically-inspired approach. The implemented spiking network relies on synaptic plasticity (long-term potentiation and long-term depression) to adapt and cope with perturbations in the manipulation scenario: changes in dynamics and kinematics of the simulated robot. Furthermore, the effect of several degrees of noise in the cerebellar input pathway (mossy fibers) was assessed depending on the employed control architecture. The implemented cerebellar model managed to adapt in the three control architectures to different dynamics and kinematics providing corrective actions for more accurate movements. According to the obtained results, coupling both control architectures (forward&recurrent) provides benefits of the two of them and leads to a higher robustness against noise. 2026-03-01T09:35:28Z 2026-03-01T09:35:28Z 2011 journal article Luque, N. R., Garrido, J. A., Carrillo, R. R., Tolu, S., & Ros, E. (2011). Adaptive cerebellar spiking model embedded in the control loop: Context switching and robustness against noise. International journal of neural systems, 21(05), 385-401. https://hdl.handle.net/10481/111730 10.1142/S0129065711002900 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional