A minimalistic representation model for head direction system
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Abstract (arXiv)
We present a minimalistic representation model for the head direction (HD) system, aiming to learn a high-dimensional representation of head direction that captures essential properties of HD cells. Our model is a representation of rotation group U(1), and we study both the fully connected version and convolutional version. We demonstrate the emergence of Gaussian-like tuning profiles and a 2D circle geometry in both versions of the model. We also demonstrate that the learned model is capable of accurate path integration.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 27 references that are in the map. First posted on arXiv 15 Nov 2024.
Builds on
6 of 27 references.
References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2019Universality and individuality in neural dynamics across large populations of recurrent networkscited by 61
- 2019Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networkscited by 9
- 2024On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embeddingcited by 6
- 2022Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cellscited by 5
- 2023Emergence of Grid-like Representations by Training Recurrent Networks with Conformal Normalizationcited by 2
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.