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Learning Hybrid System Models for Supervisory Decoding of Discrete State, with applications to the Parietal Reach Region

Hudson, Nicolas and Burdick, Joel W. (2007) Learning Hybrid System Models for Supervisory Decoding of Discrete State, with applications to the Parietal Reach Region. In: 3rd International IEEE/EMBS Conference on Neural Engineering. IEEE , Piscataway, NJ, pp. 587-592. ISBN 978-1-4244-0791-0 . https://resolver.caltech.edu/CaltechAUTHORS:20100921-152504320

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Abstract

Based on Gibbs sampling, a novel method to identify mathematical models of neural activity in response to temporal changes of behavioral or cognitive state is presented. This work is motivated by the developing field of neural prosthetics, where a supervisory controller is required to classify activity of a brain region into suitable discrete modes. Here, neural activity in each discrete mode is modeled with nonstationary point processes, and transitions between modes are modeled as hidden Markov models. The effectiveness of this framework is first demonstrated on a simulated example. The identification algorithm is then applied to extracellular neural activity recorded from multi-electrode arrays in the parietal reach region of a rhesus monkey, and the results demonstrate the ability to decode discrete changes even from small data sets.


Item Type:Book Section
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http://dx.doi.org/10.1109/CNE.2007.369741 DOIArticle
Additional Information:© 2007 IEEE.
Record Number:CaltechAUTHORS:20100921-152504320
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20100921-152504320
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:20080
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:24 Sep 2010 21:37
Last Modified:03 Oct 2019 02:05

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