JSM 2011 Online Program

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Abstract Details

Activity Number: 114
Type: Topic Contributed
Date/Time: Monday, August 1, 2011 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #302414
Title: Spike Train Kernel Methods for Neuroscience
Author(s): Il Memming Park*+ and Sohan Seth and Jose C. Principe
Companies: The University of Texas at Austin and University of Florida and University of Florida
Address: Center for Perceptual Systems, Austin, TX, 78712-0187,
Keywords: point process ; kernel method ; spike train ; neuroscience ; characteristic kernel

Positive definite kernels has been widely used in the context of machine learning by the, so called, kernel machines such as the support vector machine and the kernel principal component analysis. An attractive property of a kernel machine is that it can be applied to arbitrary spaces as long as appropriate kernel is provided. We have developed spike train kernels and analyzed their properties in the context of two-sample problem, probability embedding as well as regression and classification. We discuss strictly positive definite kernels that provide theoretical foundation for its power.

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