A data-driven framework for neural field modeling
| dc.contributor.author | Freestone, Dean | |
| dc.contributor.author | Aram, Parham | |
| dc.contributor.author | Dewar, Michael | |
| dc.contributor.author | Scerri, Kenneth | |
| dc.contributor.author | Grayden, David | |
| dc.contributor.author | Kadirkamanathan, Visakan | |
| dc.date.accessioned | 2017-08-23T06:08:26Z | |
| dc.date.available | 2017-08-23T06:08:26Z | |
| dc.date.issued | 2011-02 | |
| dc.description.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. | en_US |
| dc.description.sponsorship | Dean Freestone would like to thank Dr. Mark van Rossum for hosting him while visiting the University of Edinburgh where this work was undertaken and Dr. Matthias Hennig for useful discussions and feedback. He would also like to thank Dr. James Fallon for assistance with the observation kernel experiment. This research was funded in part by the Australian Research Council (Linkage Project LP0560684). The Bionic Ear Institute acknowledges the support it receives from the Victorian State Government through the Operational Infrastructure Support Program. Dean Freestone would like to thank the University of Melbourne's Scholarships Office for the Postgraduate Overseas Research Experience Scholarship and the Harold Mitchell Foundation for a traveling scholarship for supporting this research. Parham Aram would also like to thank Andrew Hills for useful discussions and assistance with code and the generation of figures. The authors would also like to thank Dr. John Terry for insightful discussions and feedback. Finally, the authors would like to thank the anonymous reviewers who have provided a critique that has led to valuable insights that would have gone otherwise astray. | en_US |
| dc.identifier.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 | en_US |
| dc.identifier.uri | http://repository.bionicsinstitute.org:8080/handle/123456789/254 | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier Ltd. | en_US |
| dc.subject | Neural field model | en_US |
| dc.subject | Nonlinear estimation | en_US |
| dc.subject | Intracortical connectivity | en_US |
| dc.subject | Nonlinear dynamics | en_US |
| dc.title | A data-driven framework for neural field modeling | en_US |
| dc.type | Article | en_US |