A topological study of repetitive co-activation networks in in vitro cortical assemblies. (5th January 2015)

• Record Type: Journal Article
• Authors: Pirino, Virginia; Riccomagno, Eva; Martinoia, Sergio; Massobrio, Paolo
• Is Part Of: Physical biology. Volume 12:Number 1(2015:Feb.)
• Publication Date: 2015-01-05
• Subjects: betti numbers -- co-activity -- micro-electrode arrays -- neuronal dynamics -- simplicial complex; 02.40.Re -- 87.85.D; Biophysics -- Periodicals; Biochemistry -- Periodicals; Biology -- Data processing -- Periodicals; 570.5
• DOI: 10.1088/1478-3975/12/1/016007
• Languages: English
• ISSNs: 1478-3967

Abstract

Abstract: To address the issue of extracting useful information from large data-set of large scale networks of neurons, we propose an algorithm that involves both algebraic-statistical and topological tools. We investigate the electrical behavior of in vitro cortical assemblies both during spontaneous and stimulus-evoked activity coupled to Micro-Electrode Arrays (MEAs). Our goal is to identify core sub-networks of repetitive and synchronous patterns of activity and to characterize them. The analysis is performed at different resolution levels using a clustering algorithm that reduces the network dimensionality. To better visualize the results, we provide a graphical representation of the detected sub-networks and characterize them with a topological invariant, i.e. the sequence of Betti numbers computed on the associated simplicial complexes. The results show that the extracted sub-populations of neurons have a more heterogeneous firing rate with respect to the entire network. Furthermore, the comparison of spontaneous and stimulus-evoked behavior reveals similarities in the identified clusters of neurons, indicating that in both conditions similar activation patterns drive the global network activity.