A Reproducing Kernel Hilbert Space Framework for Spike Train Signal Processing

DOI10.1162/neco.2008.09-07-614zbMath1178.68452OpenAlexW2114155871WikidataQ51831608 ScholiaQ51831608MaRDI QIDQ3612128

António R. C. Paiva, Il Memming Park, Jose C. Principe

Publication date: 3 March 2009

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

Full work available at URL: https://doi.org/10.1162/neco.2008.09-07-614

zbMATH Keywords

cross-intensity kernelsspike train inner products

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (13)

Improved Similarity Measures for Small Sets of Spike Trains ⋮ Strictly Positive-Definite Spike Train Kernels for Point-Process Divergences ⋮ Multineuron spike train analysis with R-convolution linear combination kernel ⋮ Estimating summary statistics in the spike-train space ⋮ Universality in the number variance and counting statistics of the real and symplectic Ginibre ensemble ⋮ A new framework for Euclidean summary statistics in the neural spike train space ⋮ A novel identification scheme of an inverse source problem based on Hilbert reproducing kernels ⋮ Neural Decoding with Kernel-Based Metric Learning ⋮ A simple algorithm for averaging spike trains ⋮ Optimal learning rates of \(l^p\)-type multiple kernel learning under general conditions ⋮ Reconstruction of sensory stimuli encoded with integrate-and-fire neurons with random thresholds ⋮ Binless Kernel Machine: Modeling Spike Train Transformation for Cognitive Neural Prostheses ⋮ Unnamed Item

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Error-backpropagation in temporally encoded networks of spiking neurons
• A Novel Spike Distance
• A Spike-Train Probability Model
• Metric-space analysis of spike trains: theory, algorithms and application
• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
• RKHS approach to detection and estimation problems--I: Deterministic signals in Gaussian noise
• An RKHS approach to detection and estimation problems-- III: Generalized innovations representations and a likelihood-ratio formula
• Fractal‐Based Point Processes
• On Estimation of a Probability Density Function and Mode

This page was built for publication: A Reproducing Kernel Hilbert Space Framework for Spike Train Signal Processing