Abstract

Behavioural feedback is critical for learning in the cerebral cortex. However, such feedback is often not readily available. How the cerebral cortex learns efficiently despite the sparse nature of feedback remains unclear. Inspired by recent deep learning algorithms, we introduce a systems-level computational model of cerebro-cerebellar interactions. In this model a cerebral recurrent network receives feedback predictions from a cerebellar network, thereby decoupling learning in cerebral networks from future feedback. When trained in a simple sensorimotor task the model shows faster learning and reduced dysmetria-like behaviours, in line with the widely observed functional impact of the cerebellum. Next, we demonstrate that these results generalise to more complex motor and cognitive tasks. Finally, the model makes several experimentally testable predictions regarding cerebro-cerebellar task-specific representations over learning, task-specific benefits of cerebellar predictions and the differential impact of cerebellar and inferior olive lesions. Overall, our work offers a theoretical framework of cerebro-cerebellar networks as feedback decoupling machines.

Behavioral feedback is critical for learning, but it is often not available. Here, the authors introduce a deep learning model in which the cerebellum provides the cerebrum with feedback predictions, thereby facilitating learning, reducing dysmetria, and making several experimental predictions.

Details

Title
Cerebro-cerebellar networks facilitate learning through feedback decoupling
Author
Boven, Ellen 1 ; Pemberton, Joseph 2 ; Chadderton, Paul 3   VIAFID ORCID Logo  ; Apps, Richard 3   VIAFID ORCID Logo  ; Costa, Rui Ponte 2   VIAFID ORCID Logo 

 University of Bristol, Bristol Computational Neuroscience Unit, Intelligent Systems Labs, SCEEM, Faculty of Engineering, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603); University of Bristol, School of Physiology, Pharmacology and Neuroscience, Faculty of Life Sciences, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603) 
 University of Bristol, Bristol Computational Neuroscience Unit, Intelligent Systems Labs, SCEEM, Faculty of Engineering, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603) 
 University of Bristol, School of Physiology, Pharmacology and Neuroscience, Faculty of Life Sciences, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603) 
Pages
51
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2760730451
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.