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

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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Scientific Reports, Nature Publishing Group, 2017, 7, pp.17812. 〈10.1038/s41598-017-18004-7〉
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Bénédicte Babayan, Aurélie Watilliaux, Guillaume Viejo, Anne-Lise Paradis, Benoît Girard, et al.. A hippocampo-cerebellar centred network for the learning and execution of sequence-based navigation. Scientific Reports, Nature Publishing Group, 2017, 7, pp.17812. 〈10.1038/s41598-017-18004-7〉. 〈hal-01668433〉

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