ArticleJournal of computational neuroscience2018
Linear-nonlinear-time-warp-poisson models of neural activity.
Article in Journal of computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 55 citations in OpenAlex.
- Pretraining of Embodied Recurrent Networks Bridges the Gap Between Artificial and Cortical Neural Activities.Biomimetics (Basel, Switzerland) · 2026Article
- Inferring brain-wide interactions using data-constrained recurrent neural network models.Neuron · 2026Article
- Uncovering internal states with a robust shared-state multi-neuron GLM-HMM framework.bioRxiv : the preprint server for biology · 2026Article
- Myelin-Proteinoids Interactions in Neural Signaling.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- ADA: A decoding algorithm for temporally-variable brain responses.Computational and structural biotechnology journal · 2025Article
- Spectral Learning of Shared Dynamics Between Generalized-Linear Processes.Advances in neural information processing systems · 2024Article
- Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks.Nature neuroscience · 2024Article
- Motor Cortex Latent Dynamics Encode Spatial and Temporal Arm Movement Parameters Independently.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex of male macaques.Nature communications · 2024Article
- Motor cortex latent dynamics encode spatial and temporal arm movement parameters independently.bioRxiv : the preprint server for biology · 2024Article
- Modeling and dissociation of intrinsic and input-driven neural population dynamics underlying behavior.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Dynamical flexible inference of nonlinear latent factors and structures in neural population activity.Nature biomedical engineering · 2024Article
- Article
- Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex.bioRxiv : the preprint server for biology · 2023Article
- Scanning behaviour in ants: an interplay between random-rate processes and oscillators.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2023Article
- Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo.Scientific reports · 2021Article
- Predictive learning as a network mechanism for extracting low-dimensional latent space representations.Nature communications · 2021Article
- Rethinking brain-wide interactions through multi-region 'network of networks' models.Current opinion in neurobiology · 2020Review
- Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping.Neuron · 2020Article
- Decoding Kinematic Information From Primary Motor Cortex Ensemble Activities Using a Deep Canonical Correlation Analysis.Frontiers in neuroscience · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
Abstract
Prominent models of spike trains assume only one source of variability - stochastic (Poisson) spiking - when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time. Neurons involved in planning may thus share an underlying time course that is not precisely locked to the actual movement. Here we combine the standard Linear-Nonlinear-Poisson (LNP) model with Dynamic Time Warping (DTW) to account for shared temporal variability. When applied to recordings from macaque premotor cortex, we find that time warping considerably improves predictions of neural activity. We suggest that such temporal variability is a widespread phenomenon in the brain which should be modeled.
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.