Consensus Indices of Two-Layered Multi-Star Networks: An Application of Laplacian Spectrum

In this article, the convergence speed and robustness of the consensus for several dual-layered star-composed multi-agent networks are studied through the method of graph spectra. The consensus-related indices, which can measure the performance of the coordination systems, refer to the algebraic connectivity of the graph and the network coherence. In particular, graph operations are introduced to construct several novel two-layered networks, the methods of graph spectra are applied to derive the network coherence for the multi-agent networks, and we find that the adherence of star topologies will make the first-order coherence of the dual-layered systems increase some constants in the sense of limit computations. In the second-order case, asymptotic properties also exist when the index is divided by the number of leaf nodes. Finally, the consensus-related indices of the duplex networks with the same number of nodes but non-isomorphic structures have been compared and simulated, and it is found that both the first-order coherence and second-order coherence of the network D are between A and B, and C has the best first-order robustness, but it has the worst robustness in the second-order case.

Dynamical efficiency for multimodal time-varying transportation networks

Spatial systems that experience congestion can be modeled as weighted networks whose weights dynamically change over time with the redistribution of flows. This is particularly true for urban transportation networks. The aim of this work is to find appropriate network measures that are able to detect critical zones for traffic congestion and bottlenecks in a transportation system. We propose for both single and multi-layered networks a path-based measure, called dynamical efficiency, which computes the travel time differences under congested and free-flow conditions. The dynamical efficiency quantifies the reachability of a location embedded in the whole urban traffic condition, in lieu of a myopic description based on the average speed of single road segments. In this way, we are able to detect the formation of congestion seeds and visualize their evolution in time as well-defined clusters. Moreover, the extension to multilayer networks allows us to introduce a novel measure of centrality, which estimates the expected usage of inter-modal junctions between two different transportation means. Finally, we define the so-called dilemma factor in terms of number of alternatives that an interconnected transportation system offers to the travelers in exchange for a small increase in travel time. We find macroscopic relations between the percentage of extra-time, number of alternatives and level of congestion, useful to quantify the richness of trip choices that a city offers. As an illustrative example, we show how our methods work to study the real network of a megacity with probe traffic data.

A Computational Study on Synaptic Plasticity Regulation and Information Processing in Neuron-Astrocyte Networks

Postsynaptic ionotropic receptors critically shape synaptic currents and underpin their activity-dependent plasticity. In recent years, regulation of expression of these receptors by slow inward and outward currents mediated by gliotransmitter release from astrocytes has come under scrutiny as a potentially important mechanism for the regulation of synaptic information transfer. In this study, we consider a model of astrocyte-regulated synapses to investigate this hypothesis at the level of layered networks of interacting neurons and astrocytes. Our simulations hint that gliotransmission sustains the transfer function across layers, although it decorrelates the neuronal activity from the signal pattern. Overall, our results make clear how astrocytes could transform neuronal activity by inducing a lowfrequency modulation of postsynaptic activity.

Layer-Skipping Connections Improve the Effectiveness of Equilibrium Propagation on Layered Networks

Equilibrium propagation is a learning framework that marks a step forward in the search for a biologically-plausible implementation of deep learning, and could be implemented efficiently in neuromorphic hardware. Previous applications of this framework to layered networks encountered a vanishing gradient problem that has not yet been solved in a simple, biologically-plausible way. In this paper, we demonstrate that the vanishing gradient problem can be mitigated by replacing some of a layered network's connections with random layer-skipping connections in a manner inspired by small-world networks. This approach would be convenient to implement in neuromorphic hardware, and is biologically-plausible.

Crash behavior modeling and analysis on two interdependent networks

In real-world systems, one random failure or targeted attack may lead to the cascading destruction within a system, and even create the systemic collapse among multiple correlated ones. With the help of network science, modeling the cascade failure on complex networks has become a challenging topic. In this paper, we put forward an improved cascading model on two interdependent networks to further explore the impact of the specific detachment logic on the system collapse, where the distinct condition to leave the system for a pair of nodes exists on the two-layered networks. Meanwhile, once the detachment logic is satisfied, two different criteria are adopted to determine whether this pair of nodes will leave the interdependent networks. Through extensive numerical simulations, we analyze the effects of detachment logic, network topology, departure criteria and nodal coupling relationship between layers in detail. It is found that in our detachment logic, both criteria will render the whole system to exhibit the phenomenon of pseudo-steady state and sudden collapse. In particular, two critical thresholds to characterize the evolution of system crashing emerge, which is different from previous findings under other detachment logic conditions on two-layered networks. Current results are conducive to further understanding the crashing behaviors of interdependent networks and designing the more robust networked systems in practice.