ArticleFrontiers in computational neuroscience2020
Variational Online Learning of Neural Dynamics.
Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Attractor-Based Models for Sequences and Pattern Generation in Neural Circuits.Neural computation · 2026Article
- Dynamic Compression Flows for Neuroscience Data.bioRxiv : the preprint server for biology · 2026Article
- Article
- Article
- A unifying perspective on neural manifolds and circuits for cognition.Nature reviews. Neuroscience · 2023Review
- Metastable dynamics of neural circuits and networks.Applied physics reviews · 2022Review
- Bubblewrap: Online tiling and real-time flow prediction on neural manifolds.Advances in neural information processing systems · 2021Article
- Gated Recurrent Units Viewed Through the Lens of Continuous Time Dynamical Systems.Frontiers in computational neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
New technologies for recording the activity of large neural populations during complex behavior provide exciting opportunities for investigating the neural computations that underlie perception, cognition, and decision-making. Non-linear state space models provide an interpretable signal processing framework by combining an intuitive dynamical system with a probabilistic observation model, which can provide insights into neural dynamics, neural computation, and development of neural prosthetics and treatment through feedback control. This brings with it the challenge of learning both latent neural state and the underlying dynamical system because neither are known for neural systems
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.