Unlocking the Diverse Dynamics of Large-Scale Networks

Thinking of energy grids, transportation graphs, and the brain, it becomes clear that networks are ubiquitous in nature and technology. The dynamics of networks is extremely difficult to grasp and exploit technologically because of large numbers of elements involved. A new experimental paradigm could now solve this issue. Recently, visiting graduate student David Rosin, post-doctoral research associate Damien Rontani, and Prof. Daniel Gauthier in the Quantum Electron Lab have implemented on electronic chips versatile dynamic network that can be used to solve technological problems. One study (together with David’s co-advisor, Prof. Eckehard Schöll at TU Berlin) introduces a novel artificial neural network that is promising for fast bio-inspired processing because of its fast timescale, which is a million times greater than for its biological counterpart. The network displays rich synchronization patterns that originate from a novel control scheme [1]. In another study, a network with chaotic dynamics is created that generates physical random numbers at an unprecedented bitrate, thus enabling more security and speed for up-to-date cryptographic protocols [2]. These two experiments expand our fundamental understanding of complex networks and represent a significant step towards applying dynamical networks to solve cutting-edge problems in information processing. [1] Rosin et al., Phys. Rev. Lett. 110, 104102 (2013). [2] Rosin et al., Phys. Rev. E 87, 040902(R) (2013).