A real-time spiking cerebellum model for learning robot control.
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.
Carrillo
Richard R
RR
Department of Computer Architecture and Technology, ETSI Informática y de Telecomunicación, University of Granada, Spain. rcarrillo@atc.ugr.es
Ros
Eduardo
E
Boucheny
Christian
C
Coenen
Olivier J-M D
OJ
D000200:Action Potentials / Q000502:physiology; D001185:Artificial Intelligence; D002531:Cerebellum / Q000502:physiology*; D003198:Computer Simulation; D008954:Models, Biological; D009415:Nerve Net; D009474:Neurons / Q000502:physiology*; D012371:Robotics / Q000379:methods*; D013997:Time Factors