ReviewJournal of computational neuroscience2010
A new look at state-space models for neural data.
Review in Journal of computational neuroscience, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers.
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Who cites it
102 citing papers in PubMed.
- Granger sensori-behavioral functional taxonomy of neuronal ensemble activity from two-photon calcium imaging data.PLoS computational biology · 2026Article
- Experience-dependent modulation of collective behavior in larval zebrafish.Nature communications · 2026Article
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- Stiefel Manifold Dynamical Systems for Tracking Representational Drift.bioRxiv : the preprint server for biology · 2026Article
- Uncovering Dynamic Neural Information Flow with Continuous-Time Weighted Dynamic Bayesian Networks.bioRxiv : the preprint server for biology · 2026Article
- State-Dependent Dissociation of Shared Input and Directed Information Flow in the Visual Cortex.bioRxiv : the preprint server for biology · 2026Article
- Exploring synergies: Advancing neuroscience with machine learning.Signal processing · 2026Article
- Graph theory methods for analyzing functional connectivity in multiple spike trains: application to data recorded from the visual cortex of a cat.Cognitive neurodynamics · 2025Article
- Identifying the factors governing internal state switches during nonstationary sensory decision-making.Nature communications · 2025Article
- Decoding hidden goal-directed navigational states and their neuronal representations using a novel labyrinth paradigm and probabilistic modeling framework.bioRxiv : the preprint server for biology · 2025Article
- DREDge: robust motion correction for high-density extracellular recordings across species.Nature methods · 2025Article
- Article
- Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems.ArXiv · 2025Article
- Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling.Advances in neural information processing systems · 2025Article
- Marked point process variational autoencoder with applications to unsorted spiking activities.PLoS computational biology · 2024Article
- Spectral Learning of Shared Dynamics Between Generalized-Linear Processes.Advances in neural information processing systems · 2024Article
- Regularizing hyperparameters of interacting neural signals in the mouse cortex reflect states of arousal.PLoS computational biology · 2024Article
- Calibrating Bayesian Decoders of Neural Spiking Activity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model.PLoS computational biology · 2024Article
- TIME-VARYINGData science in science · 2024Article
42 more citing papers are in PubMed but not listed here.
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8 authors.
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Abstract
State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely on certain approximations which are not always accurate. Here we review direct optimization methods that avoid these approximations, but that nonetheless retain the computational efficiency of the approximate methods. We discuss a variety of examples, applying these direct optimization techniques to problems in spike train smoothing, stimulus decoding, parameter estimation, and inference of synaptic properties. Along the way, we point out connections to some related standard statistical methods, including spline smoothing and isotonic regression. Finally, we note that the computational methods reviewed here do not in fact depend on the state-space setting at all; instead, the key property we are exploiting involves the bandedness of certain matrices. We close by discussing some applications of this more general point of view, including Markov chain Monte Carlo methods for neural decoding and efficient estimation of spatially-varying firing rates.
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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.