Abstract
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 language | English |
|---|---|
| Pages (from-to) | 133-144 |
| Number of pages | 12 |
| Journal | Journal of Neuroscience Methods |
| Volume | 145 |
| Issue number | 1-2 |
| DOIs | |
| State | Published - 30 Jun 2005 |
Bibliographical note
Funding Information:We thank Moshe Nakar for help with the construction of the experimental setup, Varda Sharkansky for technical help, and Itay Asher and Rotem Drori for help in carrying out the experiments. This research was supported in part by a Center of Excellence grant (8006/00) administered by the Israel Science Foundation, the Horowitz Foundation, RICH center, GIF, and DIP.
Keywords
- Circular statistics
- Confidence limits
- Information theory
- Monkey recordings
- Non-parametric statistics
- Prehension
- Premotor cortex
- Spatial organization