Applying resampling methods to neurophysiological data

Eran Stark, Moshe Abeles

Research output: Contribution to journalArticlepeer-review


Standard statistical techniques do not always provide answers to complex physiological questions because often there are no parametric or non-parametric distributions on which significance can be estimated. Resampling methods provide a battery of tests that can be used in such circumstances. In the past few years these methods have been explored theoretically and are now employed frequently. In this paper we describe a unified framework for the use of such methods in the context of neurophysiological data analysis. We construct specific tests for placing confidence limits on estimates of mutual information and on parameters of circular data, and we present procedures for testing hypotheses on circular and on partitioned data. These tests are explained in detail and illustrated with real data from experiments with behaving monkeys.

Original languageEnglish
Pages (from-to)133-144
Number of pages12
JournalJournal of Neuroscience Methods
Issue number1-2
StatePublished - 30 Jun 2005
Externally publishedYes


  • Circular statistics
  • Confidence limits
  • Information theory
  • Monkey recordings
  • Non-parametric statistics
  • Prehension
  • Premotor cortex
  • Spatial organization

ASJC Scopus subject areas

  • General Neuroscience


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