Neural mass spatio-temporal modeling from high-density electrode array recordings

Alyson K. Fletcher; Jon Viventi; Sundeep Rangan

doi:10.1109/ITA.2015.7309007

Neural mass spatio-temporal modeling from high-density electrode array recordings

Alyson K. Fletcher, Jon Viventi, Sundeep Rangan

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Neural mass models provide an attractive framework for modeling complex behavior in cortical circuits. The models are based on describing the dynamics of large neural populations through the space and time evolution of a small number of key aggregate statistical quantities. Fitting these models to electrode array recordings can provide insight into connectivity and structure of neural circuits as well as the response of these circuits to stimuli. However, neural mass models are fundamentally nonlinear dynamical systems with large numbers of hidden states, and validating the models on actual recordings and estimating the key parameters remains challenging. This work proposes a novel method for systematically identifying neural mass models that is particularly well-suited for high-density micro-electrocorticographic (μECoG) data. The methodology requires minimal assumptions on the model, and can automatically uncover the underlying components in the neural populations We discuss possible applications to in vivo recordings from feline visual cortex using a recently-developed, high-density 360 contact flexible electrode array with 500 μm inter-electrode spacing.

Original language	English (US)
Title of host publication	2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	319-321
Number of pages	3
ISBN (Electronic)	9781479971954
DOIs	https://doi.org/10.1109/ITA.2015.7309007
State	Published - Oct 27 2015
Event	Information Theory and Applications Workshop, ITA 2015 - San Diego, United States Duration: Feb 1 2015 → Feb 6 2015

Publication series

Name	2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings

Other

Other	Information Theory and Applications Workshop, ITA 2015
Country/Territory	United States
City	San Diego
Period	2/1/15 → 2/6/15

Keywords

Arrays
Biological system modeling
Brain models
Electrodes
Integrated circuit modeling
Sociology

ASJC Scopus subject areas

Computer Science Applications
Information Systems

Access to Document

10.1109/ITA.2015.7309007

Cite this

Fletcher, A. K., Viventi, J., & Rangan, S. (2015). Neural mass spatio-temporal modeling from high-density electrode array recordings. In 2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings (pp. 319-321). Article 7309007 (2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITA.2015.7309007

Neural mass spatio-temporal modeling from high-density electrode array recordings. / Fletcher, Alyson K.; Viventi, Jon; Rangan, Sundeep.
2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 319-321 7309007 (2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fletcher, AK, Viventi, J & Rangan, S 2015, Neural mass spatio-temporal modeling from high-density electrode array recordings. in 2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings., 7309007, 2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 319-321, Information Theory and Applications Workshop, ITA 2015, San Diego, United States, 2/1/15. https://doi.org/10.1109/ITA.2015.7309007

Fletcher AK, Viventi J, Rangan S. Neural mass spatio-temporal modeling from high-density electrode array recordings. In 2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 319-321. 7309007. (2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings). doi: 10.1109/ITA.2015.7309007

Fletcher, Alyson K. ; Viventi, Jon ; Rangan, Sundeep. / Neural mass spatio-temporal modeling from high-density electrode array recordings. 2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 319-321 (2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings).

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abstract = "Neural mass models provide an attractive framework for modeling complex behavior in cortical circuits. The models are based on describing the dynamics of large neural populations through the space and time evolution of a small number of key aggregate statistical quantities. Fitting these models to electrode array recordings can provide insight into connectivity and structure of neural circuits as well as the response of these circuits to stimuli. However, neural mass models are fundamentally nonlinear dynamical systems with large numbers of hidden states, and validating the models on actual recordings and estimating the key parameters remains challenging. This work proposes a novel method for systematically identifying neural mass models that is particularly well-suited for high-density micro-electrocorticographic (μECoG) data. The methodology requires minimal assumptions on the model, and can automatically uncover the underlying components in the neural populations We discuss possible applications to in vivo recordings from feline visual cortex using a recently-developed, high-density 360 contact flexible electrode array with 500 μm inter-electrode spacing.",

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N2 - Neural mass models provide an attractive framework for modeling complex behavior in cortical circuits. The models are based on describing the dynamics of large neural populations through the space and time evolution of a small number of key aggregate statistical quantities. Fitting these models to electrode array recordings can provide insight into connectivity and structure of neural circuits as well as the response of these circuits to stimuli. However, neural mass models are fundamentally nonlinear dynamical systems with large numbers of hidden states, and validating the models on actual recordings and estimating the key parameters remains challenging. This work proposes a novel method for systematically identifying neural mass models that is particularly well-suited for high-density micro-electrocorticographic (μECoG) data. The methodology requires minimal assumptions on the model, and can automatically uncover the underlying components in the neural populations We discuss possible applications to in vivo recordings from feline visual cortex using a recently-developed, high-density 360 contact flexible electrode array with 500 μm inter-electrode spacing.

AB - Neural mass models provide an attractive framework for modeling complex behavior in cortical circuits. The models are based on describing the dynamics of large neural populations through the space and time evolution of a small number of key aggregate statistical quantities. Fitting these models to electrode array recordings can provide insight into connectivity and structure of neural circuits as well as the response of these circuits to stimuli. However, neural mass models are fundamentally nonlinear dynamical systems with large numbers of hidden states, and validating the models on actual recordings and estimating the key parameters remains challenging. This work proposes a novel method for systematically identifying neural mass models that is particularly well-suited for high-density micro-electrocorticographic (μECoG) data. The methodology requires minimal assumptions on the model, and can automatically uncover the underlying components in the neural populations We discuss possible applications to in vivo recordings from feline visual cortex using a recently-developed, high-density 360 contact flexible electrode array with 500 μm inter-electrode spacing.

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Neural mass spatio-temporal modeling from high-density electrode array recordings

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Keywords

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