ArticleNature methods2018
Inferring single-trial neural population dynamics using sequential auto-encoders.
Article in Nature methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00912041 (BrainGate2), which is not on this map. Cited by 353 papers.
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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.
BrainGate2: Feasibility Study of an Intracortical Neural Interface System for Persons With Tetraplegia
Who cites it
353 citing papers in PubMed.
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- Task-Parametrized dynamics: Representation of time and decisions in recurrent neural networks.Journal of computational neuroscience · 2026Article
- Inferring brain-wide interactions using data-constrained recurrent neural network models.Neuron · 2026Article
- Constructing mesoscale functionomics by neural dynamics subspace clustering.National science review · 2026Article
- Manifold-constrained plasticity enables stable learning in recurrent neural circuits.PLoS computational biology · 2026Article
- Data-driven reduced modeling of neural dynamics.Nature communications · 2026Article
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- Shared latent representations of speech production for cross-patient speech decoding.Nature communications · 2026Article
- Learning-related population dynamics in right and left dorsal premotor cortex during typing skill acquisition.bioRxiv : the preprint server for biology · 2026Article
- Anticipatory organization of neural population dynamics speeds behavioral decisions.bioRxiv : the preprint server for biology · 2026Article
- Uncovering internal states with a robust shared-state multi-neuron GLM-HMM framework.bioRxiv : the preprint server for biology · 2026Article
- Long-term unsupervised recalibration of cursor-based intracortical brain-computer interfaces using a hidden Markov model.Nature biomedical engineering · 2026Article
- Spatially Masked Regression Reveals Local and Distributed Predictability in Electrophysiological Recordings.ArXiv · 2026Article
- Benchmarking criteria to determine latent linear dimensionality in neural data.Scientific reports · 2026Article
- Decoding Naturalistic Episodic Memory with Artificial Intelligence and Brain-Machine Interface.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
293 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors.
Funding
Abstract
Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.
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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.