Discriminating deterministic versus stochastic dynamics in neuronal activity

Steven J. Schiff; Tim Sauer; Taeun Chang

doi:10.1007/BF02691329

Discriminating deterministic versus stochastic dynamics in neuronal activity

Steven J. Schiff, Tim Sauer, Taeun Chang

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

34 Scopus citations

Abstract

An approach to discriminating deterministic versus stochastic dynamics from neuronal data is presented. Direct tests for determinism are emphasized, as well as using time series with clear physical correlates measured from small ensembles of neurons. Surrogate data are used to provide null hypotheses that the dynamics in our data could be accounted for by linear stochastic systems. Algorithms are given in full, and the analysis of an experimental example is given.

Original language	English (US)
Pages (from-to)	246-261
Number of pages	16
Journal	Integrative Physiological and Behavioral Science
Volume	29
Issue number	3
DOIs	https://doi.org/10.1007/BF02691329
State	Published - Jul 1994

All Science Journal Classification (ASJC) codes

General Neuroscience
Neuropsychology and Physiological Psychology

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10.1007/BF02691329

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title = "Discriminating deterministic versus stochastic dynamics in neuronal activity",

abstract = "An approach to discriminating deterministic versus stochastic dynamics from neuronal data is presented. Direct tests for determinism are emphasized, as well as using time series with clear physical correlates measured from small ensembles of neurons. Surrogate data are used to provide null hypotheses that the dynamics in our data could be accounted for by linear stochastic systems. Algorithms are given in full, and the analysis of an experimental example is given.",

author = "Schiff, {Steven J.} and Tim Sauer and Taeun Chang",

year = "1994",

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AU - Sauer, Tim

AU - Chang, Taeun

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AB - An approach to discriminating deterministic versus stochastic dynamics from neuronal data is presented. Direct tests for determinism are emphasized, as well as using time series with clear physical correlates measured from small ensembles of neurons. Surrogate data are used to provide null hypotheses that the dynamics in our data could be accounted for by linear stochastic systems. Algorithms are given in full, and the analysis of an experimental example is given.

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JO - Integrative Physiological and Behavioral Science

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Discriminating deterministic versus stochastic dynamics in neuronal activity

Abstract

All Science Journal Classification (ASJC) codes

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