ArticleAdvances in neural information processing systems2017
Gaussian process based nonlinear latent structure discovery in multivariate spike train data.
Article in Advances in neural information processing systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- Tracking the Fidelity of Internal Neural Representations with Error-In-Variables Regression.bioRxiv : the preprint server for biology · 2026Article
- Partitioning Neural Co-Variability.ArXiv · 2026Article
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- When predict can also explain: Few-shot prediction to select better neural latents.PLoS computational biology · 2025Article
- Improved inference of latent neural states from calcium imaging data.bioRxiv : the preprint server for biology · 2025Article
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- Fast Multigroup Gaussian Process Factor Models.Neural computation · 2025Article
- BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data.... International Conference on Learning Representations · 2025Article
- Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders.PLoS computational biology · 2024Article
- Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks.Nature neuroscience · 2024Article
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- Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding.Neural computation · 2024Article
- Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model.PLoS computational biology · 2024Article
- Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding.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
- Estimating neuronal firing density: A quantitative analysis of firing rate map algorithms.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A large body of recent work focuses on methods for extracting low-dimensional latent structure from multi-neuron spike train data. Most such methods employ either linear latent dynamics or linear mappings from latent space to log spike rates. Here we propose a doubly nonlinear latent variable model that can identify low-dimensional structure underlying apparently high-dimensional spike train data. We introduce the
Identifiers
31244512PMC6594561What 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.