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Concepts & Background

  • Concepts & Background
    • Suggested learning path
    • How the concepts map to nSTAT
      • Microelectrode recordings: spikes and the LFP
      • Spike trains and point-process GLMs
      • The LFP and spectral analysis
      • Goodness-of-fit and decoding
      • State-space models: learning dynamics and EM
      • Network connectivity and functional coupling
      • Uncertainty and confidence intervals
      • Rhythmic firing and the clinical microelectrode
      • Population geometry: from single neurons to neural manifolds
      • From filters to deep learning
      • Further study
      • Self-check: test your understanding
      • Common pitfalls & FAQ
      • Glossary
      • Annotated bibliography

Documentation

  • Neural Spike Train Analysis Toolbox (nSTAT)
    • Documentation Navigation
    • Key Capabilities
    • Citation
    • Lab Websites
  • Paper-Aligned Toolbox Map
    • Class Hierarchy and Object Model
    • Fitting and Assessment Workflow
    • Simulation Workflow
    • State-Space GLM (SSGLM) Workflow
    • Decoding Workflow
    • Signal Processing Methods
    • Example-to-Paper Section Mapping
  • Class Definitions
    • Signal and Covariate Primitives
      • SignalObj (nstat.SignalObj)
      • Covariate (nstat.Covariate)
      • ConfidenceInterval (nstat.ConfidenceInterval)
      • CovColl (nstat.CovColl)
    • Spiking Data Structures
      • nspikeTrain (nstat.nspikeTrain)
      • nstColl (nstat.nstColl)
      • History (nstat.History)
      • Events (nstat.Events)
    • Experiment and Configuration Objects
      • Trial (nstat.Trial)
      • TrialConfig (nstat.TrialConfig)
      • ConfigColl (nstat.ConfigColl)
    • Modeling and Inference
      • CIF (nstat.CIF)
      • LinearCIF (nstat.LinearCIF)
      • Analysis (nstat.Analysis)
      • FitResult (nstat.FitResult)
      • FitResSummary (nstat.FitResSummary)
      • PopulationTimeRescaleResult (nstat.PopulationTimeRescaleResult)
      • DecodingAlgorithms (nstat.DecodingAlgorithms)
      • DecoderSuite (nstat.DecoderSuite)
      • PoissonGLMResult (nstat.PoissonGLMResult)
    • Simulation Objects
      • PointProcessSimulation (nstat.PointProcessSimulation)
      • NetworkSimulationResult (nstat.NetworkSimulationResult)
    • Spatial / spatiotemporal point processes
      • BSplineBasis2D (nstat.extras.spatial.basis.BSplineBasis2D)
      • MaternPrior (nstat.extras.spatial.MaternPrior)
      • WaveAnalysisResult (nstat.extras.spatial.WaveAnalysisResult)
  • Example Index
    • Paper examples
    • By concept — where to start
    • Workflow tutorials
      • Simulation & example data
      • Fitting & analysis
      • Decoding & networks
    • Object & class references
    • Supplementary checks
  • Documentation Setup
    • Install and Configure
    • Build and Refresh the Search Database
    • Documentation Entry Points
    • Troubleshooting
  • API Reference
    • Core data primitives
    • Collections
    • Experiment and configuration
    • Modeling and inference
    • Decoding
    • Simulation
    • Plot style
    • Installation and data
    • MATLAB bridge (optional)
    • Exceptions and warnings
  • Extras — opt-in bridges to the Python neuro ecosystem
    • Narrative usage guides
      • nstat.extras.interop.neo — Neo bridge
      • nstat.extras.interop.pynapple — pynapple bridge
      • nstat.extras.interop.nwb — NWB:N reader
      • nstat.extras.validation.nemos_bridge — NeMoS GLM cross-validation
      • nstat.extras.validation.pykalman_bridge — pykalman Kalman cross-validation
      • nstat.extras.validation.statsmodels_bridge — statsmodels GLM cross-validation
      • nstat.extras.metrics.spike_distances — PySpike spike-train distances
      • nstat.extras.em.dynamax_bridge — EM-trained state-space models via Dynamax
      • nstat.extras.decoding.clusterless_bridge — Clusterless point-process decoding
      • nstat.extras.decoding.place_field_decoder — Place-cell encoding + 2-D PPAF decoding
      • nstat.extras.spatial — Spatial & spatiotemporal point processes
      • nstat.extras.latents.gpfa_bridge
      • nstat.extras.matlab_rng — MATLAB-aligned Mersenne Twister RNG
    • Interop — data-model bridges
    • Validation — cross-validation oracles
    • Metrics — modern spike-train distance metrics
    • EM — state-space models trained by expectation-maximization
    • Decoding — Bayesian point-process decoders
    • Spatial — spatial & spatiotemporal point processes
    • Latents — latent-variable / dimensionality-reduction bridges
  • Data Installation
    • Command line
    • Python API
    • MATLAB-compatible paper-data helper
    • Notes
  • nSTAT Python Paper Examples
    • Run Everything
    • Example Index
      • example01
      • example02
      • example03
      • example04
      • example05
      • example06
      • example07
      • example08
    • Gallery
      • Example 01: mEPSC Poisson Models Under Constant and Washout Magnesium
      • Example 02: Whisker Stimulus GLM With Lag and History Selection
      • Example 03: PSTH and SSGLM Dynamics Example
      • Example 04: Place-Cell Receptive Fields (Gaussian vs Zernike)
      • Example 05: Stimulus Decoding With PPAF and PPHF
      • Example 06: 2-D Place-Field Recovery With a B-Spline GLM Basis and an LGCP Comparator
      • Example 07: Spatiotemporal Wave Analysis of a Synthetic Planar-Wave Hawkes Adjacency
      • Example 08: Real Place-Cell Encoding-and-Decoding With Held-Out Spatial GoF
nSTAT Python
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