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Computational cognitive models of spatial memory in navigation space: a review.

Tamas Madl1, Ke Chen2, Daniela Montaldi3

  • 1School of Computer Science, University of Manchester, Manchester M13 9PL, UK; Austrian Research Institute for Artificial Intelligence, Vienna A-1010, Austria.

Neural Networks : the Official Journal of the International Neural Network Society
|February 10, 2015
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Summary

This study reviews computational models of spatial memory, crucial for navigation. It compares symbolic, neural network, and cognitive architecture models, suggesting neuroscience methods to verify their biological plausibility.

Keywords:
Computational cognitive modelingSpatial memory models

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Spatial memory is essential for navigation and environmental understanding in embodied agents.
  • Computational models aim to replicate and understand spatial memory mechanisms.
  • Existing models vary in their approach, representation, and biological plausibility.

Purpose of the Study:

  • To review and compare computational models of spatial memory.
  • To analyze models based on their level of modeling, representation, accuracy, generality, and complexity.
  • To explore potential neural mappings and propose methods for verifying biological plausibility.

Main Methods:

  • Categorization of models into symbolic, neural network, and systems-level cognitive architectures.
  • Comparative analysis across dimensions like representation, accuracy, and abstraction.
  • Review of experimental neuroscience methods for validation.

Main Results:

  • Different model categories offer distinct strengths in simulating spatial memory.
  • Few models explicitly detail neural mappings, limiting direct biological validation.
  • A framework for comparing models and suggesting validation approaches is presented.

Conclusions:

  • Computational models provide valuable insights into spatial memory.
  • Integrating neural substrate information can enhance model plausibility.
  • Further research is needed to bridge computational modeling and experimental neuroscience for spatial memory.