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Development of an Audio-based Virtual Gaming Environment to Assist with Navigation Skills in the Blind
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Navigating with grid and place cells in cluttered environments.

Vegard Edvardsen1, Andrej Bicanski2, Neil Burgess2

  • 1Department of Computer Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.

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Summary
This summary is machine-generated.

This study introduces a novel navigation model integrating grid cells for vector navigation and place/border cells for obstacle avoidance. This combined approach enhances navigation in complex, cluttered environments, especially when agents get stuck.

Keywords:
entorhinal cortexgrid cellshippocampusplace cellsspatial navigation

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Modeling

Background:

  • The hippocampus contains place cells and grid cells crucial for navigation.
  • Place cells support topological navigation, while grid cells enable metric vector navigation.
  • Vector navigation is limited in obstacle-rich environments.

Purpose of the Study:

  • To propose a computational model for navigation in complex environments.
  • To integrate grid cell-based vector navigation with obstacle avoidance using place and border cells.
  • To demonstrate how place cell replay can resolve navigation failures.

Main Methods:

  • Developed a computational model integrating grid, place, and border cell functions.
  • Simulated agent navigation in environments with obstacles.
  • Incorporated place cell replay for subgoal setting when navigation fails.
  • Tested the model's performance in experimental maze environments.

Main Results:

  • The combined model successfully navigated complex, obstacle-filled environments.
  • The model demonstrated effectiveness in underexplored territories.
  • Place cell replay facilitated overcoming navigation failures.
  • Simulated agents navigated maze environments consistent with cognitive mapping literature.

Conclusions:

  • Integrating vector navigation with local obstacle avoidance and experience-dependent topological mapping supports robust navigation.
  • The model provides a framework for understanding cognitive map formation and navigation strategies.
  • This approach can guide future experiments linking neural activity to navigational behavior.