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

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Understanding how animals translate self-motion into goal-directed actions is key to cognitive neuroscience.
• Exploration and navigation heavily rely on integrating self-motion cues with environmental information.

Purpose of the Study:

• To investigate the cognitive architecture of self-motion processing during exploration and goal-directed behavior.
• To identify the neural networks and computational mechanisms underlying sequence-based navigation using self-motion information.

Main Methods:

• Behavioral experiments in mice using environments with limited external landmarks.
• Post-behavioral analysis including c-Fos imaging for brain network characterization.
• Graph theory analysis and computational modeling of the learning process.

Main Results:

• Exploration involved widespread brain networks centered in the cerebral cortex and basal ganglia.
• Sequence-based navigation relied on networks dominated by hippocampal and cerebellar activity.
• A computational model combining memory and reinforcement learning explained the learning process.

Conclusions:

• Distinct brain networks support exploration versus sequence-based navigation driven by self-motion.
• Hippocampal and cerebellar structures play a central role in learning to translate self-motion into goal-directed actions.