Internal Models in the Cerebellum: A Coupling Scheme for Online and Offline Learning in Procedural Tasks
Metadata
Show full item recordEditorial
Springer
Materia
Inteligencia artificial Artificial intelligence
Date
2010Referencia bibliográfica
Published version: Passot, JB., Luque, N., Arleo, A. (2010). Internal Models in the Cerebellum: A Coupling Scheme for Online and Offline Learning in Procedural Tasks. In: Doncieux, S., Girard, B., Guillot, A., Hallam, J., Meyer, JA., Mouret, JB. (eds) From Animals to Animats 11. SAB 2010. Lecture Notes in Computer Science(), vol 6226. Springer, Berlin, Heidelberg. [https://doi.org/10.1007/978-3-642-15193-4_41]
Abstract
The cerebellum plays a major role in motor control. It is thought
to mediate the acquisition of forward and inverse internal models of the bodyenvironment
interaction [1]. In this study, the main processing components of
the cerebellar microcomplex are modelled as a network of spiking neural populations.
The model cerebellar circuit is shown to be suitable for learning both
forward and inverse models. A new coupling scheme is put forth to optimise online
adaptation and support offline learning. The proposed model is validated on
two procedural tasks and the simulation results are consistent with data from human
experiments on adaptive motor control and sleep-dependent consolidation
[2, 3]. This work corroborates the hypothesis that both forward and inverse internal
models can be learnt and stored by the same cerebellar circuit, and that their
coupling favours online and offline learning of procedural memories.