ArticleNature methods2022
A large-scale neural network training framework for generalized estimation of single-trial population dynamics.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- Article
- Meta-learning is expressed through altered prefrontal cortical dynamics.bioRxiv : the preprint server for biology · 2026Article
- Joint modelling of brain and behaviour dynamics with artificial intelligence.Nature reviews. Neuroscience · 2026Review
- Leveraging insights from neuroscience to build adaptive artificial intelligence.Nature neuroscience · 2026Review
- Article
- When predict can also explain: Few-shot prediction to select better neural latents.PLoS computational biology · 2025Article
- Model Predictive Control on the Neural Manifold.Neural computation · 2025Article
- Visual context affects the perceived timing of tactile sensations elicited through intracortical microstimulation: a case study of two participants.Journal of neurophysiology · 2025Article
- Spinal interneuron population dynamics underlying flexible pattern generation.Nature communications · 2025Article
- Article
- Slow Transition to Low-Dimensional Chaos in Heavy-Tailed Recurrent Neural Networks.bioRxiv : the preprint server for biology · 2025Article
- Improved inference of latent neural states from calcium imaging data.bioRxiv : the preprint server for biology · 2025Article
- Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets.ArXiv · 2025Article
- Improved interpretability in LFADS models using a learned, context-dependent per-trial bias.bioRxiv : the preprint server for biology · 2025Article
- Accurate Identification of Communication Between Multiple Interacting Neural Populations.ArXiv · 2025Article
- A doubly stochastic renewal framework for partitioning spiking variability.Nature communications · 2025Article
- Article
- Following the robot's lead: Predicting human and robot movement from EEG in a motor learning HRI task.iScience · 2025Article
- Accurate Identification of Communication Between Multiple Interacting Neural Populations.Proceedings of machine learning research · 2025Article
- High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Achieving state-of-the-art performance with deep neural population dynamics models requires extensive hyperparameter tuning for each dataset. AutoLFADS is a model-tuning framework that automatically produces high-performing autoencoding models on data from a variety of brain areas and tasks, without behavioral or task information. We demonstrate its broad applicability on several rhesus macaque datasets: from motor cortex during free-paced reaching, somatosensory cortex during reaching with perturbations, and dorsomedial frontal cortex during a cognitive timing task.
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