# Cross-Session Decoding of Neural Spiking Data via Task-Conditioned Latent Alignment

arXiv: https://arxiv.org/abs/2601.19963 · HTML: https://mlsanity.com/paper/2601.19963/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Enabling hyperparameter optimization in sequential autoencoders for spiking neural data](https://mlsanity.com/paper/1908.07896/) (2019)
- [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)
- [Energy-based Autoregressive Generation for Neural Population Dynamics](https://mlsanity.com/paper/2511.17606/) (2025)

## Built on by (most cited first, 1 of 1)

- [Stable Neural Decoding Across Sessions via Task-Conditioned Latent Alignment for Brain-Machine Interfaces](https://mlsanity.com/paper/2609.27441/) (2026)

## Abstract (arXiv)

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.