A real-time spiking cerebellum model for learning robot control Carrillo Sánchez, Richard Rafael Ros Vidal, Eduardo Boucheny, Christian Coenen, Olivier J.-M. D. Spiking neuron Cerebellum Adaptive Simulation Learning Inferior olive Probabilistic Robots real time We describe a neural network model of the cerebellum based on integrate-and-fire spiking neurons with conductance-based synapses. The neuron characteristics are derived from our earlier detailed models of the different cerebellar neurons. We tested the cerebellum model in a real-time control application with a robotic platform. Delays were introduced in the different sensorimotor pathways according to the biological system. The main plasticity in the cerebellar model is a spike-timing dependent plasticity (STDP) at the parallel fiber to Purkinje cell connections. This STDP is driven by the inferior olive (IO) activity, which encodes an error signal using a novel probabilistic low frequency model. We demonstrate the cerebellar model in a robot control system using a target-reaching task. We test whether the system learns to reach different target positions in a non-destructive way, therefore abstracting a general dynamics model. To test the system's ability to self-adapt to different dynamical situations, we present results obtained after changing the dynamics of the robotic platform significantly (its friction and load). The experimental results show that the cerebellar-based system is able to adapt dynamically to different contexts. 2025-01-31T20:53:57Z 2025-01-31T20:53:57Z 2008 journal article Carrillo, R. R., Ros, E., Boucheny, C., & Olivier, J. M. C. (2008). A real-time spiking cerebellum model for learning robot control. Biosystems, 94(1-2), 18-27 https://hdl.handle.net/10481/101706 10.1016/j.biosystems.2008.05.008 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional