A Disentangled Low-Rank RNN Framework for Uncovering Neural Connectivity and Dynamics
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917086568120320 |
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| author | Li, Chengrui Wang, Yunmiao Wang, Yule Li, Weihan Jaeger, Dieter Wu, Anqi |
| author_facet | Li, Chengrui Wang, Yunmiao Wang, Yule Li, Weihan Jaeger, Dieter Wu, Anqi |
| contents | Low-rank recurrent neural networks (lrRNNs) are a class of models that uncover low-dimensional latent dynamics underlying neural population activity. Although their functional connectivity is low-rank, it lacks disentanglement interpretations, making it difficult to assign distinct computational roles to different latent dimensions. To address this, we propose the Disentangled Recurrent Neural Network (DisRNN), a generative lrRNN framework that assumes group-wise independence among latent dynamics while allowing flexible within-group entanglement. These independent latent groups allow latent dynamics to evolve separately, but are internally rich for complex computation. We reformulate the lrRNN under a variational autoencoder (VAE) framework, enabling us to introduce a partial correlation penalty that encourages disentanglement between groups of latent dimensions. Experiments on synthetic, monkey M1, and mouse voltage imaging data show that DisRNN consistently improves the disentanglement and interpretability of learned neural latent trajectories in low-dimensional space and low-rank connectivity over baseline lrRNNs that do not encourage partial disentanglement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13899 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Disentangled Low-Rank RNN Framework for Uncovering Neural Connectivity and Dynamics Li, Chengrui Wang, Yunmiao Wang, Yule Li, Weihan Jaeger, Dieter Wu, Anqi Neurons and Cognition Computational Engineering, Finance, and Science Machine Learning Low-rank recurrent neural networks (lrRNNs) are a class of models that uncover low-dimensional latent dynamics underlying neural population activity. Although their functional connectivity is low-rank, it lacks disentanglement interpretations, making it difficult to assign distinct computational roles to different latent dimensions. To address this, we propose the Disentangled Recurrent Neural Network (DisRNN), a generative lrRNN framework that assumes group-wise independence among latent dynamics while allowing flexible within-group entanglement. These independent latent groups allow latent dynamics to evolve separately, but are internally rich for complex computation. We reformulate the lrRNN under a variational autoencoder (VAE) framework, enabling us to introduce a partial correlation penalty that encourages disentanglement between groups of latent dimensions. Experiments on synthetic, monkey M1, and mouse voltage imaging data show that DisRNN consistently improves the disentanglement and interpretability of learned neural latent trajectories in low-dimensional space and low-rank connectivity over baseline lrRNNs that do not encourage partial disentanglement. |
| title | A Disentangled Low-Rank RNN Framework for Uncovering Neural Connectivity and Dynamics |
| topic | Neurons and Cognition Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2511.13899 |