A Rate and History-Preserving Resampling Algorithm for Neural Spike Trains

DOI10.1162/neco.2008.03-08-730zbMath1160.92008OpenAlexW2102416433WikidataQ34726513 ScholiaQ34726513MaRDI QIDQ3628008

Stuart Geman, Matthew T. Harrison

Publication date: 19 May 2009

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3065177

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Neural biology (92C20) Dynamic programming (90C39)

Related Items (9)

Monosynaptic inference via finely-timed spikes ⋮ Accelerated Spike Resampling for Accurate Multiple Testing Controls ⋮ Impact of Spike Train Autostructure on Probability Distribution of Joint Spike Events ⋮ Single-Snippet Analysis for Detection of Postspike Effects ⋮ Spatiotemporal Conditional Inference and Hypothesis Tests for Neural Ensemble Spiking Precision ⋮ Spike-Centered Jitter Can Mistake Temporal Structure ⋮ Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains ⋮ Detection of dependence patterns with delay ⋮ Unnamed Item

Cites Work

• A Spike-Train Probability Model
• Dynamic programming and the graphical representation of error-correcting codes
• Testing for and Estimating Latency Effects for Poisson and Non-Poisson Spike Trains
• Sequential Monte Carlo Methods for Statistical Analysis of Tables

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