Networks of Random Trees as a Model of Neuronal Connectivity

Détails

ID Serval
serval:BIB_2CFD2F093A46
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Networks of Random Trees as a Model of Neuronal Connectivity
Périodique
Journal of Mathematical Biology
Auteur(s)
Ajazi F., Chavez-Demoulin V., Tatyana T.
Statut éditorial
Publié
Date de publication
24/07/2019
Peer-reviewed
Oui
Volume
79
Pages
1639–1663
Langue
anglais
Résumé
We provide an analysis of a randomly grown 2-d network which models the morphological growth of dendritic and axonal arbors. From the stochastic geometry of this model
we derive a dynamic graph of potential synaptic connections. We estimate standard net-
work parameters such as degree distribution, average shortest path length and clustering
coeffcient, considering all these parameters as functions of time. Our results show that
even a simple model with just a few parameters is capable of representing a wide spec-
tra of architecture, capturing properties of well-known models, such as random graphs
or small world networks, depending on the time of the network development. The introduced model allows not only rather straightforward simulations but it is also amenable
to a rigorous analysis. This provides a base for further study of formation of synaptic
connections on such networks and their dynamics due to plasticity.
Création de la notice
01/07/2019 16:37
Dernière modification de la notice
21/04/2020 5:19
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