ArticleNeuron2026
Exploiting correlations across trials and behavioral sessions to improve neural decoding.
Article in Neuron, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Lightning Pose 3D: an uncertainty-aware framework for data-efficient multi-view animal pose estimation.bioRxiv : the preprint server for biology · 2026Article
- Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining.ArXiv · 2026Article
- Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets.ArXiv · 2025Article
- Neural Encoding and Decoding at Scale.ArXiv · 2025Article
- Article
- In vivo cell-type and brain region classification via multimodal contrastive learning.bioRxiv : the preprint server for biology · 2025Article
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11 authors.
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Abstract
Traditional neural decoders link neural activity to behavior within single trials of a session, overlooking correlations across trials and sessions. However, animals show similar neural patterns when performing the same task, and their behaviors are influenced by prior experiences. To capture these dependencies, we introduce two complementary models: a multi-session reduced-rank regression model that shares behaviorally relevant neural structure across sessions and a multi-session state-space model that captures behavioral structure across trials and sessions. On 433 sessions spanning 270 brain regions in the International Brain Laboratory (IBL) mouse Neuropixels dataset, our decoders outperform traditional approaches on four behaviors, with results generalizing across datasets, species, and tasks. Unlike deep learning methods, our models are efficient and interpretable, providing low-dimensional neural representations, task-related single-neuron contributions, and brain-wide timescales of neural activation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.