Hierarchical bayesian modeling and Markov chain Monte Carlo sampling for tuning-curve analysis

Beau Cronin, Ian H. Stevenson, Mriganka Sur, Konrad P. Körding

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

A central theme of systems neuroscience is to characterize the tuning of neural responses to sensory stimuli or the production of movement. Statistically, we often want to estimate the parameters of the tuning curve, such as preferred direction, as well as the associated degree of uncertainty, characterized by error bars. Here we present a new sampling-based, Bayesian method that allows the estimation of tuning-curve parameters, the estimation of error bars, and hypothesis testing. This method also provides a useful way of visualizing which tuning curves are compatible with the recorded data. We demonstrate the utility of this approach using recordings of orientation and direction tuning in primary visual cortex, direction of motion tuning in primary motor cortex, and simulated data.

Original languageEnglish (US)
Pages (from-to)591-602
Number of pages12
JournalJournal of neurophysiology
Volume103
Issue number1
DOIs
StatePublished - Jan 2010

ASJC Scopus subject areas

  • Neuroscience(all)
  • Physiology

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