A minimalistic representation model for head direction system
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910981366480896 |
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| author | Zhao, Minglu Xu, Dehong Kong, Deqian Zhang, Wen-Hao Wu, Ying Nian |
| author_facet | Zhao, Minglu Xu, Dehong Kong, Deqian Zhang, Wen-Hao Wu, Ying Nian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10596 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A minimalistic representation model for head direction system Zhao, Minglu Xu, Dehong Kong, Deqian Zhang, Wen-Hao Wu, Ying Nian Neurons and Cognition Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 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. |
| title | A minimalistic representation model for head direction system |
| topic | Neurons and Cognition Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.10596 |