Longitudinal and Large Scale Characterization of Freely Self-organized Cultured Neural Networks (Póster)

Abstract: 

The study of how an assembly of isolated neurons self-organizes to form a complex neural network is a fundamental problem to be addressed [1]. Previous studies highlighted that the organization of the neuronal network before reaching its mature state is not random, being instead characterized by a high clustering and short paths [2]. In vitro primary cultures of dissociated invertebrate neurons from locust ganglia are used to investigate the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, modular, networks. In particular, we developed a complete software for the identification of neurons and neurites location, able to ultimately extract an adjacency matrix from each image of the culture. This, on its turn, allowed us to perform statistical analyses of some relevant network topological observables at different stages of the culture’s development, and to quantify the main characteristics of a generic assembly of isolated neurons when it self-organizes to form a complex neural network. Time evolution of associated micro- and meso-scales of the graph is reported, which allows to draw first conclusions on the main mechanisms involved in the large-scale evolution of the network’ connectiveness.


Publication type: 
Congress
Published in: 
XXXIII Dynamics Days Europe Madrid (España).
ISBN/ISSN: 
978-84-15302-43-8
Publication date: 
June 2013
CeDInt Authors: 
Other Authors: 
Daniel de Santos Sierra, Irene Sendiña Nadal, Inmaculada Leyva, Juan Antonio Almendral, Sarit Anava, Amir Ayali, Stefano Boccaletti.