Authors
BM. Babayan, A Watilliaux, G Viejo, A-L Paradis, B Girard, L Rondi-Reig
Lab
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
Journal
Scientific Reports
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.
BIOSEB Instruments Used:
Smart 3.0 - Video Tracking System (SMART30)