Probing the Nonlinearity in Neural Systems Using Cross-frequency Coherence Framework

Yuan Yang; Alfred C. Schouten; Teodoro Solis-Escalante; Frans C.T. van der Helm

doi:10.1016/j.ifacol.2015.12.326

Probing the Nonlinearity in Neural Systems Using Cross-frequency Coherence Framework

Yuan Yang, Alfred C. Schouten, Teodoro Solis-Escalante, Frans C.T. van der Helm

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

Abstract

Neural systems can present various types of nonlinear input-output relationships, such as harmonic, subharmonic, and/or intermodulation coupling. This paper aims to introduce a general framework in frequency domain for detecting and characterizing nonlinear coupling in neural systems, called the cross-frequency coherence framework (CFCF). CFCF is an extension of classic coherence based on higher-order statistics. We demonstrate an application of CFCF for identifying nonlinear interactions in human motion control. Our results indicate that CFCF can effectively characterize nonlinear properties of the afferent sensory pathway. We conclude that CFCF contributes to identifying nonlinear transfer in neural systems.

Original language	English (US)
Pages (from-to)	1386-1390
Number of pages	5
Journal	IFAC-PapersOnLine
Volume	48
Issue number	28
DOIs	https://doi.org/10.1016/j.ifacol.2015.12.326
State	Published - 2015
Externally published	Yes

Keywords

Biological Systems
Frequency Domain Identification
Nonlinear System Identification

ASJC Scopus subject areas

Control and Systems Engineering

Online availability

10.1016/j.ifacol.2015.12.326

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title = "Probing the Nonlinearity in Neural Systems Using Cross-frequency Coherence Framework",

abstract = "Neural systems can present various types of nonlinear input-output relationships, such as harmonic, subharmonic, and/or intermodulation coupling. This paper aims to introduce a general framework in frequency domain for detecting and characterizing nonlinear coupling in neural systems, called the cross-frequency coherence framework (CFCF). CFCF is an extension of classic coherence based on higher-order statistics. We demonstrate an application of CFCF for identifying nonlinear interactions in human motion control. Our results indicate that CFCF can effectively characterize nonlinear properties of the afferent sensory pathway. We conclude that CFCF contributes to identifying nonlinear transfer in neural systems.",

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author = "Yuan Yang and Schouten, {Alfred C.} and Teodoro Solis-Escalante and {van der Helm}, {Frans C.T.}",

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doi = "10.1016/j.ifacol.2015.12.326",

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AU - Schouten, Alfred C.

AU - Solis-Escalante, Teodoro

AU - van der Helm, Frans C.T.

N1 - Funding Information: The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n° 291339 (4D-EEG project). Publisher Copyright: © 2015

PY - 2015

Y1 - 2015

N2 - Neural systems can present various types of nonlinear input-output relationships, such as harmonic, subharmonic, and/or intermodulation coupling. This paper aims to introduce a general framework in frequency domain for detecting and characterizing nonlinear coupling in neural systems, called the cross-frequency coherence framework (CFCF). CFCF is an extension of classic coherence based on higher-order statistics. We demonstrate an application of CFCF for identifying nonlinear interactions in human motion control. Our results indicate that CFCF can effectively characterize nonlinear properties of the afferent sensory pathway. We conclude that CFCF contributes to identifying nonlinear transfer in neural systems.

AB - Neural systems can present various types of nonlinear input-output relationships, such as harmonic, subharmonic, and/or intermodulation coupling. This paper aims to introduce a general framework in frequency domain for detecting and characterizing nonlinear coupling in neural systems, called the cross-frequency coherence framework (CFCF). CFCF is an extension of classic coherence based on higher-order statistics. We demonstrate an application of CFCF for identifying nonlinear interactions in human motion control. Our results indicate that CFCF can effectively characterize nonlinear properties of the afferent sensory pathway. We conclude that CFCF contributes to identifying nonlinear transfer in neural systems.

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Probing the Nonlinearity in Neural Systems Using Cross-frequency Coherence Framework

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Keywords

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