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 language | English |
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Title of host publication | NIPS 2002 |
Subtitle of host publication | Proceedings of the 15th International Conference on Neural Information Processing Systems |
Editors | Suzanna Becker, Sebastian Thrun, Klaus Obermayer |
Publisher | MIT Press Journals |
Pages | 125-132 |
Number of pages | 8 |
ISBN (Electronic) | 0262025507, 9780262025508 |
State | Published - 2002 |
Event | 15th International Conference on Neural Information Processing Systems, NIPS 2002 - Vancouver, Canada Duration: 9 Dec 2002 → 14 Dec 2002 |
Publication series
Name | NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems |
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Conference
Conference | 15th International Conference on Neural Information Processing Systems, NIPS 2002 |
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Country/Territory | Canada |
City | Vancouver |
Period | 9/12/02 → 14/12/02 |
Bibliographical note
Publisher Copyright:© NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems. All rights reserved.