Decoding stimuli from multi-source neural responses

Lin Li; John S. Choi; Joseph T. Francis; Justin C. Sanchez; Jose C. Principe

doi:10.1109/EMBC.2012.6346183

Decoding stimuli from multi-source neural responses

Lin Li, John S. Choi, Joseph T. Francis, Justin C. Sanchez, Jose C. Principe

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Spike trains and local field potentials (LFPs) are two different manifestations of neural activity recorded simultaneously from the same electrode array and contain complementary information of stimuli or behaviors. This paper proposes a tensor product kernel based decoder, which allows modeling the sample from different sources individually and mapping them onto the same reproducing kernel Hilbert space (RKHS) defined by the tensor product of the individual kernels for each source, where linear regression is conducted to identify the nonlinear mapping from the multi-type neural responses to the stimuli. The decoding results of the rat sensory stimulation experiment show that the tensor-product-kernel-based decoder outperforms the decoders with either single-type neural activities.

Original language	English
Title of host publication	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages	1331-1334
Number of pages	4
DOIs	https://doi.org/10.1109/EMBC.2012.6346183
State	Published - Dec 14 2012
Event	34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States Duration: Aug 28 2012 → Sep 1 2012

Other

Other	34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Country	United States
City	San Diego, CA
Period	8/28/12 → 9/1/12

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Signal Processing
Biomedical Engineering
Health Informatics

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Li, L, Choi, JS, Francis, JT, Sanchez, JC & Principe, JC 2012, Decoding stimuli from multi-source neural responses. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS., 6346183, pp. 1331-1334, 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012, San Diego, CA, United States, 8/28/12. https://doi.org/10.1109/EMBC.2012.6346183

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AU - Principe, Jose C.

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N2 - Spike trains and local field potentials (LFPs) are two different manifestations of neural activity recorded simultaneously from the same electrode array and contain complementary information of stimuli or behaviors. This paper proposes a tensor product kernel based decoder, which allows modeling the sample from different sources individually and mapping them onto the same reproducing kernel Hilbert space (RKHS) defined by the tensor product of the individual kernels for each source, where linear regression is conducted to identify the nonlinear mapping from the multi-type neural responses to the stimuli. The decoding results of the rat sensory stimulation experiment show that the tensor-product-kernel-based decoder outperforms the decoders with either single-type neural activities.

AB - Spike trains and local field potentials (LFPs) are two different manifestations of neural activity recorded simultaneously from the same electrode array and contain complementary information of stimuli or behaviors. This paper proposes a tensor product kernel based decoder, which allows modeling the sample from different sources individually and mapping them onto the same reproducing kernel Hilbert space (RKHS) defined by the tensor product of the individual kernels for each source, where linear regression is conducted to identify the nonlinear mapping from the multi-type neural responses to the stimuli. The decoding results of the rat sensory stimulation experiment show that the tensor-product-kernel-based decoder outperforms the decoders with either single-type neural activities.

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BT - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

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Decoding stimuli from multi-source neural responses

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