Freestone, D.R., Aram, P., Dewar, M. et al. (3 more authors) (2011) A data-driven framework for neural field modeling. NeuroImage, 56 (3). pp. 1043-1058. ISSN 1053-8119
Abstract
This paper presents a framework for creating neural field models from electrophysiological data. The Wilson and Cowan or Amari style neural field equations are used to form a parametric model, where the parameters are estimated from data. To illustrate the estimation framework, data is generated using the neural field equations incorporating modeled sensors enabling a comparison between the estimated and true parameters. To facilitate state and parameter estimation, we introduce a method to reduce the continuum neural field model using a basis function decomposition to form a finite-dimensional state-space model. Spatial frequency analysis methods are introduced that systematically specify the basis function configuration required to capture the dominant characteristics of the neural field. The estimation procedure consists of a two-stage iterative algorithm incorporating the unscented Rauch–Tung–Striebel smoother for state estimation and a least squares algorithm for parameter estimation. The results show that it is theoretically possible to reconstruct the neural field and estimate intracortical connectivity structure and synaptic dynamics with the proposed framework.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2011 Elsevier. This is an author produced version of a paper subsequently published in NeuroImage. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Neural field model; Nonlinear estimation; Intracortical connectivity; Nonlinear dynamics; MASS MODEL; EEG; DYNAMICS; TIME; POPULATION; TRANSITION; GENERATION; RESPONSES; NETWORKS; EQUATION |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Jun 2016 08:50 |
Last Modified: | 21 Mar 2018 16:12 |
Published Version: | http://dx.doi.org/10.1016/j.neuroimage.2011.02.027 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.neuroimage.2011.02.027 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:100739 |