Paper-Aligned Toolbox Map
This page aligns the nSTAT toolbox documentation with the original toolbox paper:
- Cajigas I, Malik WQ, Brown EN. nSTAT: Open-source neural spike train analysis toolbox for Matlab. Journal of Neuroscience Methods 211: 245-264 (2012).
- DOI: 10.1016/j.jneumeth.2012.08.009
- PMID: 22981419
Full text links:
Contents
Class Hierarchy and Object Model
nSTAT is organized around reusable signal and trial abstractions.
- Signal and covariate primitives: `SignalObj`, `Covariate`, `ConfidenceInterval`, `CovColl`
- Spiking data structures: `nspikeTrain`, `nstColl`, `History`, `Events`
- Experiment and configuration objects: `Trial`, `TrialConfig`, `ConfigColl`
- Modeling and inference objects: `CIF`, `Analysis`, `FitResult`, `FitResSummary`, `DecodingAlgorithms`
Class references and examples:
Fitting and Assessment Workflow
The paper's core workflow fits point-process GLMs and evaluates fit quality.
1. Build trial data with `Trial`, `CovColl`, and `nstColl`. 2. Define candidate models with `TrialConfig` and `ConfigColl`. 3. Fit models with `Analysis.RunAnalysisForNeuron` or `Analysis.RunAnalysisForAllNeurons`. 4. Assess goodness-of-fit using `FitResult` diagnostics (KS, residuals, confidence bands) and summarize across neurons with `FitResSummary`.
Related examples:
Simulation Workflow
The toolbox supports simulation of point-process and related neural models.
- Conditional intensity specification and simulation: `CIF`
- Thinning-based point-process simulation: Point Process Simulation via Thinning
- End-to-end simulated analysis: Simulated Explicit Stimulus and History
Decoding Workflow
nSTAT includes point-process and Gaussian-state decoding algorithms that are described in the paper's adaptive filtering sections.
- Static decoding methods: `DecodingAlgorithms`
- Example workflows: Decoding Univariate Simulated Stimuli, Decoding with History, and Decoding Bivariate Simulated Stimuli
Example-to-Paper Section Mapping
The examples below correspond directly to the paper's representative workflows.
- mEPSCAnalysis and PSTHEstimation: model-based event process analysis
- Explicit Stimulus and Place Cell Receptive Fields: stimulus-response and receptive field modeling
- DecodingExample, DecodingExampleWithHist, and StimulusDecode2D: decoding and state estimation
- nSTAT Paper Examples: consolidated reproduction workflow for paper analyses