Spatiotemporal multi-resolution approximation of the Amari type neural field model.
Identifieur interne : 001024 ( Ncbi/Curation ); précédent : 001023; suivant : 001025Spatiotemporal multi-resolution approximation of the Amari type neural field model.
Auteurs : P. Aram [Royaume-Uni] ; D R Freestone [Australie] ; M. Dewar [États-Unis] ; K. Scerri [Malte] ; V. Jirsa [France] ; D B Grayden [Australie] ; V. Kadirkamanathan [Royaume-Uni]Source :
- NeuroImage [ 1095-9572 ] ; 2013.
Descripteurs français
- KwdFr :
- MESH :
- physiologie : Encéphale.
- Algorithmes, Humains, Modèles neurologiques, Modèles théoriques.
English descriptors
- KwdEn :
- MESH :
- physiology : Brain.
- Algorithms, Humans, Models, Neurological, Models, Theoretical.
Abstract
Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic example is provided to demonstrate the framework.
DOI: 10.1016/j.neuroimage.2012.10.039
PubMed: 23116813
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<front><div type="abstract" xml:lang="en">Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic example is provided to demonstrate the framework.</div>
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