ArticleNature biomedical engineering2024
Dynamical flexible inference of nonlinear latent factors and structures in neural population activity.
Article in Nature biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Prioritized nonlinear modeling of shared dynamics across neural populations.Journal of neural engineering · 2026Article
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- An EEG-based non-linear state-space model for trust inference in a space-relevant human-autonomy teaming task.Frontiers in neuroergonomics · 2026Article
- Model Predictive Control on the Neural Manifold.Neural computation · 2025Article
- Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures.Machine learning: science and technology · 2025Review
- Article
- Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data.ArXiv · 2025Article
- Article
- Prioritized learning of cross-population neural dynamics.Journal of neural engineering · 2025Article
- Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data.Proceedings of machine learning research · 2025Article
- Probabilistic Geometric Principal Component Analysis with application to neural data.... International Conference on Learning Representations · 2025Article
- BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data.... International Conference on Learning Representations · 2025Article
- Less is more: selection from a small set of options improves BCI velocity control.Journal of neural engineering · 2025Article
- Latent profile analysis of fear of progression in obstructive sleep apnea hypoventilation syndrome patients: a cross-sectional study.BMC psychology · 2025Article
- Computation-through-Dynamics Benchmark: Simulated datasets and quality metrics for dynamical models of neural activity.bioRxiv : the preprint server for biology · 2025Article
- Exploring the suitability of piecewise-linear dynamical system models for cognitive neural dynamics.Frontiers in neuroscience · 2025Article
- BaRISTA: Brain Scale Informed Spatiotemporal Representation of Human Intracranial Neural Activity.Advances in neural information processing systems · 2025Article
- Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling.Advances in neural information processing systems · 2025Article
- Dynamical modeling of nonlinear latent factors in multiscale neural activity with real-time inference.Advances in neural information processing systems · 2025Article
- Spectral Learning of Shared Dynamics Between Generalized-Linear Processes.Advances in neural information processing systems · 2024Article
Corrections and comments
- Erratum issued
- Update of
Authors and funding
4 authors.
Funding
Abstract
Modelling the spatiotemporal dynamics in the activity of neural populations while also enabling their flexible inference is hindered by the complexity and noisiness of neural observations. Here we show that the lower-dimensional nonlinear latent factors and latent structures can be computationally modelled in a manner that allows for flexible inference causally, non-causally and in the presence of missing neural observations. To enable flexible inference, we developed a neural network that separates the model into jointly trained manifold and dynamic latent factors such that nonlinearity is captured through the manifold factors and the dynamics can be modelled in tractable linear form on this nonlinear manifold. We show that the model, which we named 'DFINE' (for 'dynamical flexible inference for nonlinear embeddings') achieves flexible inference in simulations of nonlinear dynamics and across neural datasets representing a diversity of brain regions and behaviours. Compared with earlier neural-network models, DFINE enables flexible inference, better predicts neural activity and behaviour, and better captures the latent neural manifold structure. DFINE may advance the development of neurotechnology and investigations in neuroscience.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.