# Stable Neural Decoding Across Sessions via Task-Conditioned Latent Alignment for Brain-Machine Interfaces

arXiv: https://arxiv.org/abs/2609.27441 · HTML: https://mlsanity.com/paper/2609.27441/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Learnable latent embeddings for joint behavioral and neural analysis](https://mlsanity.com/paper/2204.00673/) (2022)
- [A Unified, Scalable Framework for Neural Population Decoding](https://mlsanity.com/paper/2310.16046/) (2023)
- [Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution](https://mlsanity.com/paper/2407.14668/) (2024)
- [Adversarial Domain Adaptation for Stable Brain-Machine Interfaces](https://mlsanity.com/paper/1810.00045/) (2018)
- [Making brain-machine interfaces robust to future neural variability](https://mlsanity.com/paper/1610.05872/) (2016)
- [Enabling hyperparameter optimization in sequential autoencoders for spiking neural data](https://mlsanity.com/paper/1908.07896/) (2019)
- [Targeted Neural Dynamical Modeling](https://mlsanity.com/paper/2110.14853/) (2021)
- [Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation](https://mlsanity.com/paper/2202.06159/) (2022)
- [Latent Diffusion for Neural Spiking Data](https://mlsanity.com/paper/2407.08751/) (2024)
- [Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment](https://mlsanity.com/paper/2505.04331/) (2025)
- [Cross-Session Decoding of Neural Spiking Data via Task-Conditioned Latent Alignment](https://mlsanity.com/paper/2601.19963/) (2026)

## Abstract (arXiv)

Achieving stable long-term neural decoding in invasive brain-machine interfaces (BMIs) remains challenging due to variations in recorded neural populations across sessions. Current latent alignment approaches may overlook task-dependent structure during cross-session adaptation. We propose Task-Conditioned Latent Alignment (TCLA), a framework that stabilizes neural decoding by learning a shared latent space. TCLA learns a low-dimensional source representation using neural reconstruction and continuous behavioral supervision. During target-session adaptation, the shared representation is fixed, while target neural activity is mapped into the source latent space by aligning source and target distributions separately for each task condition. We evaluated TCLA on seven nonhuman primate datasets spanning multiple tasks. In long-term cross-session evaluation, TCLA achieved a mean R2 of 0.476±0.014 with a negative R2 failure rate of only 6.8%. Across 1,356 within-subject session pairs, TCLA achieved a mean R2 of 0.371±0.009 with a failure rate of 6.8%. Across 2,134 cross-subject session pairs, TCLA achieved a mean R2 of 0.218±0.004 with a failure rate of 12.9%, substantially better than those of the comparison methods. These results demonstrate that by preserving behaviorally relevant and task-dependent latent structure, TCLA improves the robustness of neural decoding across recording sessions and subjects. The source code is publicly available at hrefhttps://github.com/FAMD-CASIA/TCLAhttps://github.com/FAMD-CASIA/TCLA.