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Sci Rep. 2017 Dec 19;7(1):17812. doi: 10.1038/s41598-017-18004-7.

A hippocampo-cerebellar centred network for the learning and execution of sequence-based navigation.

Author information

1
Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Neurosciences Paris Seine - Institut de Biologie Paris Seine (NPS - IBPS), Cerebellum Navigation and Memory team (CeZaMe), 75005, Paris, France.
2
Sorbonne Universités, Université Pierre et Marie Curie (UPMC), CNRS UMR 7222, Institut des Systèmes Intelligents et de Robotique (ISIR), F-75005, Paris, France.
3
Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Neurosciences Paris Seine - Institut de Biologie Paris Seine (NPS - IBPS), Cerebellum Navigation and Memory team (CeZaMe), 75005, Paris, France. laure.rondi-reig@upmc.fr.

Abstract

How do we translate self-motion into goal-directed actions? Here we investigate the cognitive architecture underlying self-motion processing during exploration and goal-directed behaviour. The task, performed in an environment with limited and ambiguous external landmarks, constrained mice to use self-motion based information for sequence-based navigation. The post-behavioural analysis combined brain network characterization based on c-Fos imaging and graph theory analysis as well as computational modelling of the learning process. The study revealed a widespread network centred around the cerebral cortex and basal ganglia during the exploration phase, while a network dominated by hippocampal and cerebellar activity appeared to sustain sequence-based navigation. The learning process could be modelled by an algorithm combining memory of past actions and model-free reinforcement learning, which parameters pointed toward a central role of hippocampal and cerebellar structures for learning to translate self-motion into a sequence of goal-directed actions.

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