Context separability mediated by the granular layer in a spiking cerebellum model for robot control

Authors:
Niceto R. Luque;Jesús A. Garrido;Richard R. Carrillo;Eduardo Ros
Affiliations:
Department of Computer Architecture and Technology, University of Granada, Periodista Daniel Saucedo s/n, Granada, Spain;Department of Computer Architecture and Technology, University of Granada, Periodista Daniel Saucedo s/n, Granada, Spain;Department of Computer Architecture and Electronics, University of Almeria Almeria, Spain;Department of Computer Architecture and Technology, University of Granada, Periodista Daniel Saucedo s/n, Granada, Spain
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Year:
2011

Citing 3
Cited 0

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Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics

Neural Computation

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Abstract

In this paper, we study how a biologically-plausible cerebellum architecture can store and retrieve different robotic-arm internal models (in synaptic connections between granular layer and Purkinje cells) at the granule layer (dynamic modifications of a base robot-arm-plant model), and how the model microstructure and input signal representations can efficiently infer models in a robot control scenario during object manipulation. More specifically, we have evaluated the contribution of the granular layer to the ability of the cerebellum to generate corrective actions. To achieve this we have embedded a spiking cerebellar model into an analog control loop whose output commands a simulated robot arm. The performance results obtained by using a cerebellum which includes granular layer are compared to those using a cerebellum without this layer. The results show that this layer effectively contributes to the generation of accurate cerebellar corrections. This work represents a well defined case of study in the field of neurobotics, in which biologically plausible neural systems and robots are used to study the functionality of biological systems.