State-Space Algorithms for Estimating Spike Rate Functions
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Brown_State-Space Algorithms.pdf
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1.48 MB
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Author(s)
Smith, Anne C.
Scalon, Joao D.
Wirth, Sylvia
Yanike, Marianna
Suzuki, Wendy A.
Brown, Emery N.
Date Issued
January 2010
Journal
Computational Intelligence and Neuroscience
Publisher
Hindawi Publishing
Citation
Anne C. Smith, Joao D. Scalon, Sylvia Wirth, Marianna Yanike, Wendy A. Suzuki, and Emery N. Brown, “State-Space Algorithms for Estimating Spike Rate Functions,” Computational Intelligence and Neuroscience, vol. 2010, Article ID 426539, 14 pages, 2010. doi:10.1155/2010/426539
Version
Final published version
Abstract
The accurate characterization of spike firing rates including the determination of when changes in activity occur is a fundamental issue in the analysis of neurophysiological data. Here we describe a state-space model for estimating the spike rate function that provides a maximum likelihood estimate of the spike rate, model goodness-of-fit assessments, as well as confidence intervals for the spike rate function and any other associated quantities of interest. Using simulated spike data, we first compare the performance of the state-space approach with that of Bayesian adaptive regression splines (BARS) and a simple cubic spline smoothing algorithm. We show that the state-space model is computationally efficient and comparable with other spline approaches. Our results suggest both a theoretically sound and practical approach for estimating spike rate functions that is applicable to a wide range of neurophysiological data.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution
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DOI of Published Version
http://dx.doi.org/10.1155/2010/426539
