Path planning in the hippocampo-prefrontal cortex pathway: an adaptive model based receding horizon planner

Area of Science:

• Cognitive Neuroscience
• Computational Neuroscience
• Neurology

Background:

• Human path planning exhibits characteristics like course/fine planning, supervision, adaptation, and complexity reduction.
• These traits are mirrored in engineering controllers, specifically the model predictive controller and receding horizon planner.

Purpose of the Study:

• To hypothesize that the human brain executes path planning using a receding horizon planner mechanism.
• To explore the neurological underpinnings of this hypothesis by mapping brain regions to planner functions.
• To investigate the implications of this hypothesis for understanding and diagnosing Alzheimer's disease.

Main Methods:

• Comparative analysis of human path planning strategies and receding horizon planner functionalities.
• Identification of corresponding brain regions (hippocampus, parietal cortex, prefrontal cortex) for planning functions.
• Examination of Alzheimer's disease patient data concerning predictive horizon and working memory deficits.

Main Results:

• The human brain utilizes a receding horizon planner-like strategy, with the hippocampus for course models and parietal cortex for fine models.
• The prefrontal cortex acts as a supervisor, with adaptive planning adjusting to environmental complexity.
• Alzheimer's patients exhibit a reduced predictive horizon, impairing robustness and planning in complex, dynamic environments.

Conclusions:

• The receding horizon planner model provides a framework for understanding human path planning and its neural basis.
• Deficits in predictive horizon and planning in Alzheimer's patients, linked to hippocampo-prefrontal pathway disruption, may serve as early diagnostic markers.
• Future research should focus on experimental validation of this hypothesis through specialized planning and prediction tests.