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 › Academic › peer-review

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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
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
Publication status	Published - 2015

Keywords

Biological Systems
Frequency Domain Identification
Nonlinear System Identification

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AU - Schouten, Alfred C.

AU - Solis-Escalante, Teodoro

AU - van der Helm, Frans C.T.

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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KW - Nonlinear System Identification

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ER -

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

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