Papers

(…) we provide a method to build models of collective dynamics from homing pigeon flight data. We show that our models follow the source dynamics well, and from them we are able to infer that significant collective behavior occurs in pigeon flights. Our results are consistent with the basic principles of previous hypotheses and models that have been proposed. Our approach serves as an initial outline towards the usage of experimental data to construct computational models to understand many complex phenomena with hypothesized collective behavior.

Proteins are remarkable machines of the living systems that show diverse biochemical functions. Biochemical diversity has grown over time via molecular evolution. In order to understand how diversity arose, it is fundamental to understand how the earliest proteins evolved and served as templates for the present diverse proteome. The one sequence - one structure - one function paradigm is being extended to a new view: an ensemble of different conformations in equilibrium can evolve new function and the analysis of inherent structural dynamics is crucial to give a more complete understanding of protein evolution.

Computational modeling and behavioral experimentation suggest that human frontal lobe function is capable of monitoring three or four concurrent behavioral strategies in order to select the most suitable one during decision-making.

Emergence of cooperation in evolutionary prisoner's dilemma game strongly depends on the topology of underlying interaction network. We explore this dependence using community networks with different levels of structural heterogeneity, which are generated by a tunable upper-bound on the total number of links that any vertex can have. We study the effect of community structure on cooperation by analyzing a finite population analogue of the evolutionary replicator dynamics. We find that structural heterogeneity mediates the effect of community structure on cooperation. In the community networks with low level of structural heterogeneity, community structure has negative effect on cooperation. However, the positive effect of community structure on cooperation appears and enhances with increasing structural heterogeneity. Our work may be helpful for understanding the complexity of cooperative behaviors in social networks.

Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network.

We study information processing in populations of Boolean networks with evolving connectivity and systematically explore the interplay between the learning capability, robustness, the network topology, and the task complexity. We solve a long-standing open question and find computationally that, for large system sizes N, adaptive information processing drives the networks to a critical connectivity Kc=2. For finite size networks, the connectivity approaches the critical value with a power law of the system size N. We show that network learning and generalization are optimized near criticality, given that the task complexity and the amount of information provided surpass threshold values. Both random and evolved networks exhibit maximal topological diversity near Kc. We hypothesize that this diversity supports efficient exploration and robustness of solutions. Also reflected in our observation is that the variance of the fitness values is maximal in critical network populations. Finally, we discuss implications of our results for determining the optimal topology of adaptive dynamical networks that solve computational tasks.

In insect colonies, different individuals specialize in different tasks related to colony maintenance and growth. Unveiling why this division of labor evolved and how individuals decide which task to take on is crucial for our understanding of complex group behavior. Here we model the evolution of general behavioral rules for processing environmental signals of task need in social insect colonies, using artificial neural networks.

This paper deals with the arrow of complexification of engineering. We claim that the complexification of engineering consists in (a) that shift throughout which engineering becomes a science; thus it ceases to be a (mere) praxis or profession; (b) becoming a science, engineering can be considered as one of the sciences of complexity. In reality, the complexification of engineering is the process by which engineering can be studied, achieved, and understood in terms of knowledge, and not of goods and services any longer. Complex engineered systems and bio-inspired engineering are so far the two expressions of a complex engineering.

The elementary cellular automaton following rule 184 can mimic particles flowing in one direction at a constant speed. Therefore, this automaton can model highway traffic qualitatively. In a recent paper, we have incorporated intersections regulated by traffic lights to this model using exclusively elementary cellular automata. In such a paper, however, we only explored a rectangular grid. We now extend our model to more complex scenarios using an hexagonal grid. This extension shows first that our model can readily incorporate multiple-way intersections and hence simulate complex scenarios. In addition, the current extension allows us to study and evaluate the behavior of two different kinds of traffic-light controller for a grid of six-way streets allowing for either two- or three-street intersections: a traffic light that tries to adapt to the amount of traffic (which results in self-organizing traffic lights) and a system of synchronized traffic lights with coordinated rigid periods (sometimes called the “green-wave” method). We observe a tradeoff between system capacity and topological complexity. The green-wave method is unable to cope with the complexity of a higher-capacity scenario, while the self-organizing method is scalable, adapting to the complexity of a scenario and exploiting its maximum capacity. Additionally, in this article, we propose a benchmark, independent of methods and models, to measure the performance of a traffic-light controller comparing it against a theoretical optimum.

The spectacle of animals moving en masse is arguably one of the most fascinating phenomena in biology. For example, schools of fish can move in an orderly manner, and then change direction abruptly or, if under pressure from a nearby predator, swirl like a vigorously stirred fluid. The non-living world also has examples of collective motion, in systems that consist of units ranging from macromolecules to metallic rods, or even robots. On page 448 of this issue, Sumino et al. describe another, until now unobserved, example of such behaviour: the coordinated motion of hundreds of thousands of subcellular structures known as microtubules, which spontaneously self-organize into a lattice-like structure of vortices. When considered in the context of about half a dozen known universal classes of collective-motion pattern, this new structure poses challenges in terms of explaining how it can arise and its relevance to applications.

We show novel techniques of analysing complex dynamics of cellular automata (CA) with chaotic behaviour. CA are well known computational substrates for studying emergent collective behaviour, complexity, randomness and interaction between order and disorder. A number of attempts have been made to classify CA functions on their spatio-temporal dynamics and to predict behavior of any given function. Examples include mechanical computation, lambda and Z-parameters, mean field theory, differential equations and number conserving features. We propose to classify CA based on their behaviour when they act in a historical mode, i.e. as CA with memory. We demonstrate that cell-state transition rules enriched with memory quickly transform a chaotic system converging to a complex global behaviour from almost any initial condition. Thus in just a few steps we can select chaotic rules without exhaustive computational experiments or recurring to additional parameters. We provide analysis of well-known chaotic functions in one-dimensional CA, and decompose dynamics of the automata using majority memory.

Biological systems often consist of multiple interacting subsystems, the brain being a prominent example. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. In this work we investigate the extent to which the cause-and-effect framework is applicable to such interacting subsystems. We base our work on a standard notion of causal effects and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We identify the constraints on the structure of the causal connections that determine the existence of natural causal effects. In particular, we show that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. Only when the causing subsystem is autonomous with respect to the rest can this interpretation be made. We note that subsystems in the brain are often bidirectionally connected, which means that interactions rarely should be quantified in terms of cause-and-effect. We furthermore introduce a framework for how natural causal effects can be characterized when they exist. Our work also has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM). Our results are generic and the concept of natural causal effects is relevant in all areas where the effects of interactions between subsystems are of interest.