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Pattern Formation in Neural Population Models

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Encyclopedia of Computational Neuroscience

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Synonyms

Turing-Hopf bifurcation

Definition

Experimental studies have shown that neural population activity may exhibit certain well-structured spatiotemporal patterns, which reflect the properties of the neurons in the underlying network. The aim of neural population models is the reconstruction of such experimentally observed patterns or their prediction.

Detailed Description

Spatial patterns in neural populations and their temporal evolution have been observed experimentally in different neural structures. They emerge due to a strong interplay between the spatial physiological structure of the population, the intrinsic time scales, and the nonlinear interaction of elements. For instance, in a neural field model, these elements typically are the spatial axonal connectivity topology, the mean synaptic time scales, the nonlinear transfer function, and the additional nonlinear interactions such as synaptic depression.

One of the most important questions in the study of pattern formation...

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References

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Author information

Authors and Affiliations

  1. Team NEUROSYS, INRIA CR Nancy – Grand Est, Villers-les-Nancy, France

    Dr. rer. nat. Axel Hutt Habilitation a Diriger des Recherche (HDR)

Authors
  1. Dr. rer. nat. Axel Hutt Habilitation a Diriger des Recherche (HDR)
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Corresponding author

Correspondence to Axel Hutt Habilitation a Diriger des Recherche (HDR) .

Editor information

Editors and Affiliations

  1. Atlanta, Georgia, USA

    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, Florida, USA

    Ranu Jung

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Hutt, A. (2013). Pattern Formation in Neural Population Models. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_72-3

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  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_72-3

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4614-7320-6

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