Applying resampling methods to neurophysiological data

Eran Stark; Moshe Abeles

doi:10.1016/j.jneumeth.2004.12.005

Applying resampling methods to neurophysiological data

Eran Stark, Moshe Abeles

Hebrew University of Jerusalem

Research output: Contribution to journal › Article › peer-review

22 Scopus citations

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	https://doi.org/10.1016/j.jneumeth.2004.12.005
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.

Funding

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.

Funders	Funder number
Center of Excellence	8006/00
Horowitz Foundation for Social Policy
German-Israeli Foundation for Scientific Research and Development
Israel Science Foundation

Keywords

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

Access to Document

10.1016/j.jneumeth.2004.12.005

Cite this

@article{c1f0b9a1930c4ef1998495252cf5bf51,

title = "Applying resampling methods to neurophysiological data",

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.",

keywords = "Circular statistics, Confidence limits, Information theory, Monkey recordings, Non-parametric statistics, Prehension, Premotor cortex, Spatial organization",

author = "Eran Stark and Moshe Abeles",

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.",

year = "2005",

month = jun,

day = "30",

doi = "10.1016/j.jneumeth.2004.12.005",

language = "אנגלית",

volume = "145",

pages = "133--144",

journal = "Journal of Neuroscience Methods",

issn = "0165-0270",

publisher = "Elsevier B.V.",

number = "1-2",

}

TY - JOUR

T1 - Applying resampling methods to neurophysiological data

AU - Stark, Eran

AU - Abeles, Moshe

N1 - 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.

PY - 2005/6/30

Y1 - 2005/6/30

N2 - 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.

AB - 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.

KW - Circular statistics

KW - Confidence limits

KW - Information theory

KW - Monkey recordings

KW - Non-parametric statistics

KW - Prehension

KW - Premotor cortex

KW - Spatial organization

UR - http://www.scopus.com/inward/record.url?scp=19544394871&partnerID=8YFLogxK

U2 - 10.1016/j.jneumeth.2004.12.005

DO - 10.1016/j.jneumeth.2004.12.005

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

C2 - 15894379

AN - SCOPUS:19544394871

SN - 0165-0270

VL - 145

SP - 133

EP - 144

JO - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

IS - 1-2

ER -

Applying resampling methods to neurophysiological data

Abstract

Bibliographical note

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this