Emergent Dynamical-Structural Interdependencies in Hippocampus Cultures.


Hippocampus is a well known structure of the mammals limbic system, which is associated to physiological responses from afferent emotional stimuli as well as memory and space orientation. Several studies have shown similar emergent properties in experiments with in vitro and in vivo cultures. Based on that, we have investigated the dynamical and topological properties of functional complex networks of several hippocampus cultures from 18-days rat embryos, aiming to identify the interplay between them. To do that, Six Multi Electrode Array experiments were designed in order to record the spikes dynamics from spontaneous activated neurons. Specifically, we study how neural networks evolve and mature (from 7 to 26 days in vitro). From the dynamical point of view, we obtain the ordinal patterns of Inter Spike Intervals (ISIs) of the cultures. With this data, we compute their respective normalized permutation Shannon’s entropy, the disequilibrium and its statistical complexity. In addition, Kuramoto order parameter was computed to capture the emergence of the synchronous behaviour of the cultures. Pearson correlation parameter between neuron phases along culture maturation is used to determine the weight of the functional connections between neurons, altogether leading to a complex functional network whose topology evolves in time. Thus, complex networks parameters such as strength, weighted clustering, local efficiency, global efficiency, averaged shortest path, and eigenvector centrality were computed to topologically characterize the evolution of the cultures. Our results revealed two different stages in the evolution of the hippocampus cultures: a growing stage and a mature demeanor. Also, due to their inherent spontaneous generation of spikes, the growing scenario depicts a high phase synchronization, which evolves to an asynchronous regime. On the other hand, networks metrics show differences correlated with the culture growth. Interestingly, we found correlations between two specific topological and dynamical metrics: the higher (lower) the clustering, the lower (higher) the entropy of the whole culture.

Publication type: 
Published in: 
NetSci 2015. Satellite: Brain Networks. Zaragoza, España, pp. 21-22
Publication date: 
June 2015
Other Authors: 
J. H. Martínez, J. M. Pastor, E. Fernández-Jover, I. SendiñaNadal, J.M. Buldú