title: A topological study of repetitive co-activation networks in in vitro cortical assemblies.
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.
authors.affiliation: Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS),University of Genova, Genova, Italy.
authors.forename: Virginia
authors.initials: V
authors.lastname: Pirino
authors.forename: Eva
authors.initials: E
authors.lastname: Riccomagno
authors.forename: Sergio
authors.initials: S
authors.lastname: Martinoia
authors.forename: Paolo
authors.initials: P
authors.lastname: Massobrio
mesh_terms: D000465:Algorithms; D000818:Animals; D002540:Cerebral Cortex / Q000166:cytology* / Q000502:physiology*; D066298:In Vitro Techniques; D008839:Microelectrodes; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D051381:Rats; D017207:Rats, Sprague-Dawley