ArticleNature neuroscience2024
Facemap: a framework for modeling neural activity based on orofacial tracking.
Article in Nature neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers.
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Who cites it
90 citing papers in PubMed.
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- Sleep reveals dynamics integrating and segregating movement and stimulus representations in V1.Nature communications · 2026Article
- DRD1 and DRD2 dopamine-sensitive neurons in the central amygdala respond differently to rewarding and aversive stimuli.Translational psychiatry · 2026Article
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- Beast3D: Animal behavioral analysis and neural encoding from multi-view video via Gaussian splatting.ArXiv · 2026Article
- Cheese3D enables sensitive detection and analysis of whole-face movement in mice.Nature neuroscience · 2026Article
- NeuroPupil: A generalization-first framework for scalable and biologically informative cross-species pupillometry.bioRxiv : the preprint server for biology · 2026Article
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- Syngap1 Synchronizes Relative Neuronal Maturation Across Cortical Areas to Organize Distributed Functional Networks.bioRxiv : the preprint server for biology · 2026Article
- Cell-type-specific sustained value representations in the claustrum.bioRxiv : the preprint server for biology · 2026Article
- Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining.ArXiv · 2026Article
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- Exploiting correlations across trials and behavioral sessions to improve neural decoding.Neuron · 2026Article
- Distinct roles of prefrontal subregion feedback to the primary visual cortex across behavioral states.Neuron · 2026Article
- The representation of omitted sounds in the mouse auditory cortex.Nature communications · 2026Article
30 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
Recent studies in mice have shown that orofacial behaviors drive a large fraction of neural activity across the brain. To understand the nature and function of these signals, we need better computational models to characterize the behaviors and relate them to neural activity. Here we developed Facemap, a framework consisting of a keypoint tracker and a deep neural network encoder for predicting neural activity. Our algorithm for tracking mouse orofacial behaviors was more accurate than existing pose estimation tools, while the processing speed was several times faster, making it a powerful tool for real-time experimental interventions. The Facemap tracker was easy to adapt to data from new labs, requiring as few as 10 annotated frames for near-optimal performance. We used the keypoints as inputs to a deep neural network which predicts the activity of ~50,000 simultaneously-recorded neurons and, in visual cortex, we doubled the amount of explained variance compared to previous methods. Using this model, we found that the neuronal activity clusters that were well predicted from behavior were more spatially spread out across cortex. We also found that the deep behavioral features from the model had stereotypical, sequential dynamics that were not reversible in time. In summary, Facemap provides a stepping stone toward understanding the function of the brain-wide neural signals and their relation to behavior.
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