Cross-Session Decoding of Neural Spiking Data via Task-Conditioned Latent Alignment
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914525656121344 |
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| author | Zhao, Canyang Peng, Bolin Mayo, J. Patrick Ju, Ce Liu, Bing |
| author_facet | Zhao, Canyang Peng, Bolin Mayo, J. Patrick Ju, Ce Liu, Bing |
| contents | Training a high-performing neural decoder can be difficult when only limited data are available from a recording session. To address this challenge, we propose a Task-Conditioned Latent Alignment framework (TCLA) for cross-session neural decoding with limited target-session data. Building upon an autoencoder architecture, TCLA first learns a low-dimensional neural representation from a source session with sufficient data. For target sessions with limited data, TCLA then aligns the target latent representations to the source session in a task-conditioned manner, enabling effective transfer of learned neural representations to support decoder training in the target session. We evaluate TCLA on the macaque motor and oculomotor center-out datasets. Compared to baseline methods trained solely on target-session data, TCLA consistently improves decoding performance across datasets and decoding settings, with gains in the coefficient of determination of up to 0.386 for y coordinate velocity decoding in a motor dataset. These results suggest that TCLA provides an effective strategy for transferring knowledge from source to target sessions, improving neural decoding performance under conditions with limited target-session data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19963 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cross-Session Decoding of Neural Spiking Data via Task-Conditioned Latent Alignment Zhao, Canyang Peng, Bolin Mayo, J. Patrick Ju, Ce Liu, Bing Machine Learning Artificial Intelligence Training a high-performing neural decoder can be difficult when only limited data are available from a recording session. To address this challenge, we propose a Task-Conditioned Latent Alignment framework (TCLA) for cross-session neural decoding with limited target-session data. Building upon an autoencoder architecture, TCLA first learns a low-dimensional neural representation from a source session with sufficient data. For target sessions with limited data, TCLA then aligns the target latent representations to the source session in a task-conditioned manner, enabling effective transfer of learned neural representations to support decoder training in the target session. We evaluate TCLA on the macaque motor and oculomotor center-out datasets. Compared to baseline methods trained solely on target-session data, TCLA consistently improves decoding performance across datasets and decoding settings, with gains in the coefficient of determination of up to 0.386 for y coordinate velocity decoding in a motor dataset. These results suggest that TCLA provides an effective strategy for transferring knowledge from source to target sessions, improving neural decoding performance under conditions with limited target-session data. |
| title | Cross-Session Decoding of Neural Spiking Data via Task-Conditioned Latent Alignment |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.19963 |