Reproducing kernel Hilbert spaces for spike train analysis

Author

Paiva, António R C ; Park, Il ; Príncipe, José C.

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL

fYear

2008

fDate

March 31 2008-April 4 2008

Firstpage

5212

Lastpage

5215

Abstract

This paper introduces a generalized cross-correlation (GCC) measure for spike train analysis derived from reproducing kernel Hilbert spaces (RKHS) theory. An estimator for GCC is derived that does not depend on binning or a specific kernel and it operates directly and efficiently on spike times. For instantaneous analysis as required for real-time use, an instantaneous estimator is proposed and proved to yield the GCC on average. We finalize with two experiments illustrating the usefulness of the techniques derived.

Keywords

Hilbert spaces; bioelectric potentials; neural nets; generalized cross-correlation measure; instantaneous estimator; kernel Hilbert spaces; spike train analysis; Biomedical computing; Biomedical engineering; Biomedical measurements; Electric variables measurement; Extraterrestrial measurements; Gain measurement; Hilbert space; Kernel; Neurons; Quantization; Spike train analysis; cross-correlation; reproducing kernel Hilbert spaces; synchrony detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on

Conference_Location

Las Vegas, NV

ISSN

1520-6149

Print_ISBN

978-1-4244-1483-3

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2008.4518834

Filename

4518834