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Title: A data-driven framework for neural field modeling
Authors: Freestone, Dean
Aram, Parham
Dewar, Michael
Scerri, Kenneth
Grayden, David
Kadirkamanathan, Visakan
Keywords: Neural field model
Nonlinear estimation
Intracortical connectivity
Nonlinear dynamics
Issue Date: Feb-2011
Publisher: Elsevier Ltd.
Citation: Freestone, D. R., P. Aram, M. Dewar, K. Scerri, D. B. Grayden, and V. Kadirkamanathan. 2011. A data-driven framework for neural field modeling. Neuroimage. 56(3): 1043-58
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.
URI: http://repository.bionicsinstitute.org:8080/handle/123456789/254
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