A minimalistic representation model for head direction system
This work addresses the challenge of understanding neural representations of head direction in neuroscience, but it appears incremental as it builds on existing models without introducing a major breakthrough.
The authors tackled the problem of modeling the head direction system by proposing a minimalistic representation model based on the rotation group U(1), which demonstrated the emergence of Gaussian-like tuning profiles, a 2D circle geometry, and accurate path integration in both fully connected and convolutional versions.
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.