Spikerneis: Embedding spiking neurons in inner-product spaces

Lavi Shpigelman, Yoram Singer, Rony Paz, Eilon Vaadia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Inner-product operators, often referred to as kernels in statistical learning, define a mapping from some input space into a feature space. The focus of this paper is the construction of biologically-motivated kernels for cortical activities. The kernels we derive, termed Spikernels, map spike count sequences into an abstract vector space in which we can perform various prediction tasks. We discuss in detail the derivation of Spikernels and describe an efficient algorithm for computing their value on any two sequences of neural population spike counts. We demonstrate the merits of our modeling approach using the Spikernel and various standard kernels for the task of predicting hand movement velocities from cortical recordings. In all of our experiments all the kernels we tested outperform the standard scalar product used in regression with the Spikernel consistently achieving the best performance.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002
PublisherNeural information processing systems foundation
ISBN (Print)0262025507, 9780262025508
StatePublished - 2003
Event16th Annual Neural Information Processing Systems Conference, NIPS 2002 - Vancouver, BC, Canada
Duration: Dec 9 2002Dec 14 2002

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other16th Annual Neural Information Processing Systems Conference, NIPS 2002
CountryCanada
CityVancouver, BC
Period12/9/0212/14/02

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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