State-space algorithms for estimating spike rate functions

Anne C. Smith, Joao D. Scalon, Sylvia Wirth, Marianna Yanike, Wendy A. Suzuki, Emery N. Brown

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number426539
JournalComputational Intelligence and Neuroscience
Volume2010
DOIs
StatePublished - Jun 18 2010

ASJC Scopus subject areas

  • Computer Science(all)
  • Neuroscience(all)
  • Mathematics(all)

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