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

Biomechatronics & Human-Machine Control

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

7 Citations (Scopus)

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
Title of host publication	IFAC-PapersOnline
Editors	Y Zhao
Publisher	Elsevier
Pages	1386-1390
Volume	48 - 28
DOIs	https://doi.org/10.1016/j.ifacol.2015.12.326
Publication status	Published - 2015
Event	17th IFAC Symposium on System Identification - Beijing, China Duration: 19 Oct 2015 → 21 Oct 2015

Conference

Conference	17th IFAC Symposium on System Identification
Abbreviated title	SYSID 2015
Country/Territory	China
City	Beijing
Period	19/10/15 → 21/10/15

Keywords

Biological Systems
Frequency Domain Identification
Nonlinear System Identification

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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.",

keywords = "Biological Systems, Frequency Domain Identification, Nonlinear System Identification",

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

year = "2015",

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

language = "English",

volume = "48 - 28",

pages = "1386--1390",

editor = "Y Zhao",

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note = "17th IFAC Symposium on System Identification, SYSID 2015 ; Conference date: 19-10-2015 Through 21-10-2015",

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T1 - Probing the nonlinearity in neural systems using cross-frequency coherence framework

AU - Yang, Yuan

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.

KW - Biological Systems

KW - Frequency Domain Identification

KW - Nonlinear System Identification

UR - http://www.scopus.com/inward/record.url?scp=84988453144&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2015.12.326

DO - 10.1016/j.ifacol.2015.12.326

M3 - Conference contribution

AN - SCOPUS:84988453144

VL - 48 - 28

SP - 1386

EP - 1390

BT - IFAC-PapersOnline

A2 - Zhao, Y

PB - Elsevier

T2 - 17th IFAC Symposium on System Identification

Y2 - 19 October 2015 through 21 October 2015

ER -

Probing the nonlinearity in neural systems using cross-frequency coherence framework

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