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Paper Detail

Paper: PS-2A.58
Session: Poster Session 2A
Location: H Lichthof
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Forward Models in the Cerebellum using Reservoirs and Perturbation Learning
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Katharina Schmid, Julien Vitay, Fred H. Hamker, Chemnitz University of Technology, Germany
Abstract: The cerebellum is thought to be able to learn forward models, which allow to predict the sensory consequences of planned movements and adapt behavior accordingly. Although classically considered as a feedforward structure learning in a supervised manner, recent proposals highlighted the importance of the internal recurrent connectivity of the cerebellum to produce rich dynamics, as well as the importance of reinforcement-like mechanisms for its plasticity. Based on these models, we propose a neuro-computational model of the cerebellum using an inhibitory reservoir architecture and biologically plausible learning mechanisms based on perturbation learning. The model is trained to predict the position of a simple robotic arm after ballistic movements. Understanding how the cerebellum is able to learn forward models might allow elucidating the biological basis of model-based reinforcement learning.