Coupled Transformer Autoencoder for Disentangling Multi-Region Neural Latent Dynamics
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915571493240832 |
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| author | Sristi, Ram Dyuthi Narasimha, Sowmya Manojna Huang, Jingya Despatin, Alice Musall, Simon Gilja, Vikash Mishne, Gal |
| author_facet | Sristi, Ram Dyuthi Narasimha, Sowmya Manojna Huang, Jingya Despatin, Alice Musall, Simon Gilja, Vikash Mishne, Gal |
| contents | Simultaneous recordings from thousands of neurons across multiple brain areas reveal rich mixtures of activity that are shared between regions and dynamics that are unique to each region. Existing alignment or multi-view methods neglect temporal structure, whereas dynamical latent variable models capture temporal dependencies but are usually restricted to a single area, assume linear read-outs, or conflate shared and private signals. We introduce the Coupled Transformer Autoencoder (CTAE) - a sequence model that addresses both (i) non-stationary, non-linear dynamics and (ii) separation of shared versus region-specific structure in a single framework. CTAE employs transformer encoders and decoders to capture long-range neural dynamics and explicitly partitions each region's latent space into orthogonal shared and private subspaces. We demonstrate the effectiveness of CTAE on two high-density electrophysiology datasets with simultaneous recordings from multiple regions, one from motor cortical areas and the other from sensory areas. CTAE extracts meaningful representations that better decode behavioral variables compared to existing approaches. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_20068 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Coupled Transformer Autoencoder for Disentangling Multi-Region Neural Latent Dynamics Sristi, Ram Dyuthi Narasimha, Sowmya Manojna Huang, Jingya Despatin, Alice Musall, Simon Gilja, Vikash Mishne, Gal Machine Learning Simultaneous recordings from thousands of neurons across multiple brain areas reveal rich mixtures of activity that are shared between regions and dynamics that are unique to each region. Existing alignment or multi-view methods neglect temporal structure, whereas dynamical latent variable models capture temporal dependencies but are usually restricted to a single area, assume linear read-outs, or conflate shared and private signals. We introduce the Coupled Transformer Autoencoder (CTAE) - a sequence model that addresses both (i) non-stationary, non-linear dynamics and (ii) separation of shared versus region-specific structure in a single framework. CTAE employs transformer encoders and decoders to capture long-range neural dynamics and explicitly partitions each region's latent space into orthogonal shared and private subspaces. We demonstrate the effectiveness of CTAE on two high-density electrophysiology datasets with simultaneous recordings from multiple regions, one from motor cortical areas and the other from sensory areas. CTAE extracts meaningful representations that better decode behavioral variables compared to existing approaches. |
| title | Coupled Transformer Autoencoder for Disentangling Multi-Region Neural Latent Dynamics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.20068 |