Inference of time series components by online co-evolution

Time series data is often composed of a multitude of individual, superimposed dynamics. We propose a novel algorithm for inferring time series compositions through evolutionary synchronization of modular networks (ESMoN). ESMoN orchestrates a set of trained dynamic modules, assuming that some of those modules’ dynamics, suitably parameterized, will be present in the targeted time series. With the help of iterative co-evolution techniques, ESMoN optimizes the activities of its modules dynamically, which effectively synchronizes the system with the unfolding time series signal and distributes the dynamic subcomponents present in the time series over the respective modules. We show that ESMoN can adapt modules of different types. Moreover, it is able to precisely identify the signal components of various time series dynamics. We thus expect that ESMoN will be useful also in other domains—including, for example, medical, physical, and behavioral data domains—where the data is composed of known signal sources.

Experimental Platform to Study Spiking Pattern Propagation in Modular Networks In Vitro

The structured organization of connectivity in neural networks is associated with highly efficient information propagation and processing in the brain, in contrast with disordered homogeneous network architectures. Using microfluidic methods, we engineered modular networks of cultures using dissociated cells with unidirectional synaptic connections formed by asymmetric microchannels. The complexity of the microchannel geometry defined the strength of the synaptic connectivity and the properties of spiking activity propagation. In this study, we developed an experimental platform to study the effects of synaptic plasticity on a network level with predefined locations of unidirectionally connected cellular assemblies using multisite extracellular electrophysiology.

Optimal resilience of modular interacting networks

Coupling between networks is widely prevalent in real systems and has dramatic effects on their resilience and functional properties. However, current theoretical models tend to assume homogeneous coupling where all the various subcomponents interact with one another, whereas real-world systems tend to have various different coupling patterns. We develop two frameworks to explore the resilience of such modular networks, including specific deterministic coupling patterns and coupling patterns where specific subnetworks are connected randomly. We find both analytically and numerically that the location of the percolation phase transition varies nonmonotonically with the fraction of interconnected nodes when the total number of interconnecting links remains fixed. Furthermore, there exists an optimal fraction r* of interconnected nodes where the system becomes optimally resilient and is able to withstand more damage. Our results suggest that, although the exact location of the optimal r* varies based on the coupling patterns, for all coupling patterns, there exists such an optimal point. Our findings provide a deeper understanding of network resilience and show how networks can be optimized based on their specific coupling patterns.

The asymptotic formula on average weighted path length for scale-free modular network

In this paper, we discuss the average path length for a class of scale-free modular networks with deterministic growth. To facilitate the analysis, we define the sum of distances from all nodes to the nearest hub nodes and the nearest peripheral nodes. For the unweighted network, we find that whether the scale-free modular network is single-hub or multiple-hub, the average path length grows logarithmically with the increase of nodes number. For the weighted network, we deduce that when the network iteration [Formula: see text] tends to infinity, the average weighted shortest path length is bounded, and the result is independent of the connection method of network.

Collective dynamics of neuronal activities in various modular networks

Three type of modular networks are constructed using polydimethylsiloxane (PDMS) microstructures fabricated on a multi-electrode array (MEA) without transfer to investigate how neuronal activities are affected by modular network structure.

Modularity and specialization in bat–fly interaction networks are remarkably consistent across patches within urbanized landscapes and spatial scales

Patterns of specialization and the structure of interactions between bats and ectoparasitic flies have been studied mostly on non-urban environments and at local scales. Thus, how anthropogenic disturbances influence species interactions and network structure in this system remain poorly understood. Here, we investigated patterns of interaction between Phyllostomidae bats and ectoparasitic Streblidae flies, and variations in network specialization and structure across Cerrado patches within urbanized landscapes in Brazil and between local and regional scales. We found high similarity in the richness and composition of bat and fly species across communities, associated with low turnover of interactions between networks. The high specialization of bat–streblid interactions resulted in little connected and modular networks, with the emergence of modules containing subsets of species that interact exclusively or primarily with each other. Such similarities in species and interaction composition and network structure across communities and scales suggest that bat–fly interactions within Cerrado patches are little affected by the degree of human modification in the surrounding matrix. This remarkable consistency is likely promoted by specific behaviors, the tolerance of Phyllostomidae bats to surrounding urbanized landscapes as well as by the specificity of the streblid–bat interactions shaped over evolutionary time.