PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Spikernels: Predicting Arm Movements by Embedding Population Spike Rate Patterns in Inner-Product Spaces
Lavi Shpigelman, Yoram Singer, Rony Paz and Eilon Vaadia
Neural Computation Volume 17, Number 3, 2005.

Abstract

Inner-product operators, often referred to as {\em 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 by comparing the Spikernel to various standard kernels in the task of predicting hand movement velocities from cortical recordings. All of the kernels that we tested in our experiments outperform the standard scalar product used in linear regression with the Spikernel consistently achieving the best performance.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Brain Computer Interfaces
ID Code:870
Deposited By:Lavi Shpigelman
Deposited On:02 January 2005