Effective connectivity matrix for neural ensembles

Qi She; Winnie K. Y. So; Rosa H. M. Chan

doi:10.1109/EMBC.2016.7591021

Effective connectivity matrix for neural ensembles

Qi She^*
, Winnie K. Y. So
, Rosa H. M. Chan

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

In this paper, we present an efficient framework to study the directional interactions within the multiple-input multiple-output (MIMO) biological neural network from spiketrain data. We used an efficient generalized linear model (GLM) with Laguerre basis functions to model a MIMO neural system, and developed an Effective Connectivity Matrix (ECM) to visualize excitatory and inhibitory connections within the neural network. A new causality representation was developed based on system dynamics. Statistical test was applied to identify the significance of the measured causality. We tested ECM on both common-input model and random networks. The results showed that ECM could (1) solve the common-input problem; (3) recover the causality among random neural networks with different connection probabilities and sizes of networks; and (3) identify the excitatory and inhibitory connections among neuronal populations accurately.

Original language	English
Title of host publication	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Publisher	IEEE
Pages	1612-1615
Volume	2016-October
ISBN (Print)	9781457702204
DOIs	https://doi.org/10.1109/EMBC.2016.7591021
Publication status	Published - 13 Oct 2016
Event	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016) - Disney's Contemporary Resort, Orlando, United States Duration: 16 Aug 2016 → 20 Aug 2016 https://embc.embs.org/2016/ https://embc.embs.org/2016/

Publication series

Name
Volume	2016-October
ISSN (Print)	1557-170X

Conference

Conference	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016)
Abbreviated title	EMBC'16
Place	United States
City	Orlando
Period	16/08/16 → 20/08/16
Internet address	https://embc.embs.org/2016/ https://embc.embs.org/2016/

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@inproceedings{7fbc08845e344551a14922bad6348d64,

title = "Effective connectivity matrix for neural ensembles",

abstract = "In this paper, we present an efficient framework to study the directional interactions within the multiple-input multiple-output (MIMO) biological neural network from spiketrain data. We used an efficient generalized linear model (GLM) with Laguerre basis functions to model a MIMO neural system, and developed an Effective Connectivity Matrix (ECM) to visualize excitatory and inhibitory connections within the neural network. A new causality representation was developed based on system dynamics. Statistical test was applied to identify the significance of the measured causality. We tested ECM on both common-input model and random networks. The results showed that ECM could (1) solve the common-input problem; (3) recover the causality among random neural networks with different connection probabilities and sizes of networks; and (3) identify the excitatory and inhibitory connections among neuronal populations accurately.",

author = "Qi She and So, \{Winnie K. Y.\} and Chan, \{Rosa H. M.\}",

year = "2016",

month = oct,

day = "13",

doi = "10.1109/EMBC.2016.7591021",

language = "English",

isbn = "9781457702204",

volume = "2016-October",

publisher = "IEEE",

pages = "1612--1615",

booktitle = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

address = "United States",

note = "38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016), EMBC'16 ; Conference date: 16-08-2016 Through 20-08-2016",

url = "https://embc.embs.org/2016/, https://embc.embs.org/2016/",

}

She, Q, So, WKY & Chan, RHM 2016, Effective connectivity matrix for neural ensembles. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. vol. 2016-October, 7591021, IEEE, pp. 1612-1615, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016), Orlando, United States, 16/08/16. https://doi.org/10.1109/EMBC.2016.7591021

Effective connectivity matrix for neural ensembles. / She, Qi; So, Winnie K. Y.; Chan, Rosa H. M.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol. 2016-October IEEE, 2016. p. 1612-1615 7591021.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Effective connectivity matrix for neural ensembles

AU - She, Qi

AU - So, Winnie K. Y.

AU - Chan, Rosa H. M.

PY - 2016/10/13

Y1 - 2016/10/13

N2 - In this paper, we present an efficient framework to study the directional interactions within the multiple-input multiple-output (MIMO) biological neural network from spiketrain data. We used an efficient generalized linear model (GLM) with Laguerre basis functions to model a MIMO neural system, and developed an Effective Connectivity Matrix (ECM) to visualize excitatory and inhibitory connections within the neural network. A new causality representation was developed based on system dynamics. Statistical test was applied to identify the significance of the measured causality. We tested ECM on both common-input model and random networks. The results showed that ECM could (1) solve the common-input problem; (3) recover the causality among random neural networks with different connection probabilities and sizes of networks; and (3) identify the excitatory and inhibitory connections among neuronal populations accurately.

AB - In this paper, we present an efficient framework to study the directional interactions within the multiple-input multiple-output (MIMO) biological neural network from spiketrain data. We used an efficient generalized linear model (GLM) with Laguerre basis functions to model a MIMO neural system, and developed an Effective Connectivity Matrix (ECM) to visualize excitatory and inhibitory connections within the neural network. A new causality representation was developed based on system dynamics. Statistical test was applied to identify the significance of the measured causality. We tested ECM on both common-input model and random networks. The results showed that ECM could (1) solve the common-input problem; (3) recover the causality among random neural networks with different connection probabilities and sizes of networks; and (3) identify the excitatory and inhibitory connections among neuronal populations accurately.

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U2 - 10.1109/EMBC.2016.7591021

DO - 10.1109/EMBC.2016.7591021

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781457702204

VL - 2016-October

SP - 1612

EP - 1615

BT - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

PB - IEEE

T2 - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016)

Y2 - 16 August 2016 through 20 August 2016

ER -

Effective connectivity matrix for neural ensembles

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