My research interests:
- Computational Neuroscience
- Structure-Dynamics relations in neuronal networks
- Information transmission in neural systems
- Object Oriented Programming
Structure and Dynamics of Complex Networks
Most complex systems ranging from the brain to ecosystems, power grids and the Internet, can be represented as large complex networks. There exist many basic dynamical processes on complex networks, such as synchronization, epidemic spreading, robustness against attacks, and so on. A central issue in current multidisciplinary research is to understand the effect of complex connectivity patterns on these dynamical processes. Understanding the relationship between the structure and dynamics of complex networks helps in predicting their collective behavior, and indicates ways to engineer networks to achieve desired dynamics and prevent undesired behaviors. But for many biological systems, such as neuronal circuits in the brain, interacting proteins or genes, the structure of the networks are largely unknown, whereas the dynamics of the individual units of the networks can be often observed. Therefore, various methods have also been proposed to solve the inverse problem of inferring the network structure from controlled measurements of its dynamics. Although in the past decade significant progress has been made in the study of networks, our understanding of real-world complex systems is far from complete. The reason why real-world networks are non-trivial is that in the way their components establish connections, any bias, however small, gives rise to structural and dynamical correlations. This makes understanding complex systems challenging but, at the same time, it means that each network contains, hidden within its structure, important cues about how the system operates and evolves. The goal of our research is to develop a theoretical framework and computational tools for understanding the relationship between structure and function of complex networks by investigating different aspects characterizing real-world complex networks. For example we will investigate the effects of the time delay (due to finite signal-propagation speeds), number, length and place of directed loops, the interplay between node dynamics and network structure on the collective behavior of the networks.