City College of New York, USA
Hernan research focuses on the theoretical understanding of Complex Systems from a Statistical Physics viewpoint. He is working towards the development of new emergent laws for complex systems, ranging from brain networks to biological networks and social systems. Treating these complex systems from a unified theoretical approach, he uses
concepts from statistical mechanics, network and optimization theory, machine learning, and big-data science to advance new views on complex systems and networks.
Essential nodes and keystone species in the brain, ecosystems and social systems
Identifying essential nodes in complex networks is a central problem for biological systems to social systems. We treat this problem in three paradigmatic cases: the brain, ecosystems and social networks. Mathematically, we find the set of influential nodes by optimizing the damage to the giant connected component with systematic inactivation of nodes. We then apply network theory and pharmacogenetic interventions in a rat brain to predict and target essential nodes responsible for global integration in a model of learning and memory. We find that the integration of the brain network is mediated by a set of weak nodes through optimization of influence in optimal percolation. Pharmacogenetic inhibitions confirm the theoretical predictions. We discuss the relevance of these influencers to ecological systems dominated by abrupt first order tipping points as well as connectomes with regularities.