Disentangling Fact from Grid Cell Fiction in Trained Deep Path Integrators
Rylan Schaeffer1, Mikail Khona2, Sanmi Koyejo1
1Computer Science, Stanford.
Arxiv
|December 18, 2023
Summary
Deep neural networks trained for spatial navigation do not naturally develop grid cell activity. Current path integration models fail to explain the biological origin of grid cells.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Artificial Intelligence
Background:
- Grid cells are crucial for spatial navigation, forming a cognitive map of an environment.
- The dominant hypothesis suggests grid cells emerge from path integration in neural networks.
- Previous research challenged this by showing path integration alone does not yield grid-like activity in deep networks.
Conclusions:
- Path integration alone is insufficient to explain the emergence of grid cells.
- Current computational models and assessment methods may not accurately reflect biological reality.
- Further research is needed to understand the true mechanisms underlying grid cell formation.
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