ArticleComputational intelligence and neuroscience2016
A Linear Analysis of Coupled Wilson-Cowan Neuronal Populations.
Article in Computational intelligence and neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- On Synchronizing Coupled Retinogeniculocortical Pathways: A Toy Model.Computational intelligence and neuroscience · 2018Article
- On the Influence of Structural Connectivity on the Correlation Patterns and Network Synchronization.Frontiers in computational neuroscience · 2018Article
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2 authors.
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Abstract
Let a neuronal population be composed of an excitatory group interconnected to an inhibitory group. In the Wilson-Cowan model, the activity of each group of neurons is described by a first-order nonlinear differential equation. The source of the nonlinearity is the interaction between these two groups, which is represented by a sigmoidal function. Such a nonlinearity makes difficult theoretical works. Here, we analytically investigate the dynamics of a pair of coupled populations described by the Wilson-Cowan model by using a linear approximation. The analytical results are compared to numerical simulations, which show that the trajectories of this fourth-order dynamical system can converge to an equilibrium point, a limit cycle, a two-dimensional torus, or a chaotic attractor. The relevance of this study is discussed from a biological perspective.
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