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Trajectory scanning as a predictive coding mechanism for goal-directed navigation, obstacle avoidance and episodic

Jennifer C Robinson1, Patrick A LaChance1, Samantha Malmberg1

  • 1Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|July 9, 2026
PubMed
Summary

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Neural trajectory scanning may enable navigation by exploring and selecting paths towards remembered locations. This mechanism, supported by rodent and human data, links spatial memory, navigation, and obstacle avoidance.

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Episodic memory and spatial representations are crucial for planning future navigation.
  • Neural mechanisms underlying trajectory planning and goal-directed navigation remain incompletely understood.

Purpose of the Study:

  • To propose theoretical mechanisms for behavioral planning based on episodic memory using neural trajectory scanning.
  • To link neural data from rodent and human studies within a predictive coding framework.

Main Methods:

  • Modeling neural trajectory scanning for exploring and selecting paths to remembered goals.
  • Integrating neurophysiological data from rodent medial entorhinal cortex and hippocampus.
  • Aligning theoretical predictions with human neuroimaging findings on egocentric representations.
Keywords:
electrophysiologyentorhinal cortexhippocampusmemoryparahippocampal cortexpostrhinal cortexretrosplenial cortexrodent

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Main Results:

  • The proposed model explains alternating spatial scans observed in rodent brains.
  • Sequential vector activation allows encoding and retrieval of spatio-temporal episodic memories.
  • Trajectory scanning facilitates collision detection and avoidance in allocentric and egocentric frames.

Conclusions:

  • Neural trajectory scanning offers a unified framework for allocentric navigation and episodic memory retrieval.
  • This mechanism accounts for egocentric neural responses like boundary and bearing cells.
  • The findings provide a predictive coding basis for integrating navigation, memory, and obstacle representation.