Strictly Positive-Definite Spike Train Kernels for Point-Process Divergences

DOI10.1162/NECO_a_00309zbMath1311.92048OpenAlexW2171445446WikidataQ42644524 ScholiaQ42644524MaRDI QIDQ2919438

Sohan Seth, Il Memming Park, Jose C. Principe, Murali Rao

Publication date: 2 October 2012

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

Full work available at URL: https://doi.org/10.1162/neco_a_00309

Mathematics Subject Classification ID

Neural biology (92C20)

Related Items (2)

Multineuron spike train analysis with R-convolution linear combination kernel ⋮ Neural Decoding with Kernel-Based Metric Learning

Cites Work

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• A test of independence based on a generalized correlation function
• An introduction to the theory of point processes
• On an extremal property of the Poisson process
• Strictly Hermitian positive definite functions
• A Novel Spike Distance
• Improved Similarity Measures for Small Sets of Spike Trains
• Discrimination with Spike Times and ISI Distributions
• A Hilbert Space Embedding for Distributions
• A Reproducing Kernel Hilbert Space Framework for Spike Train Signal Processing
• Metric-space analysis of spike trains: theory, algorithms and application
• A Unified Framework for Quadratic Measures of Independence
• Spikernels: Predicting Arm Movements by Embedding Population Spike Rate Patterns in Inner-Product Spaces
• The Entropy of a Point Process

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