Can we talk? How the cognitive neuroscience of attention emerged from neurobiology and psychology, 1980–2005

This study uses author co-citation analysis to trace prospectively the development of the cognitive neuroscience of attention between 1980 and 2005 from its precursor disciplines: cognitive psychology, single cell neurophysiology, neuropsychology, and evoked potential research. The author set consists of 28 authors highly active in attentional research in the mid-1980s. PFNETS are used to present the co-citation networks. Authors are clustered via the single-link clustering intrinsic to the PFNET algorithm. By 1990 a distinct cognitive neuroscience specialty cluster emerges, dominated by authors engaged in brain imaging research.     

Unsupervised learning of control signals and their encodings in Caenorhabditis elegans whole-brain recordings

A major goal of computational neuroscience is to understand the relationship between synapse-level structure and network-level functionality. Caenorhabditis elegans is a model organism to probe this relationship due to the historic availability of the synaptic structure (connectome) and recent advances in whole brain calcium imaging techniques. Recent work has applied the concept of network controllability to neuronal networks, discovering some neurons that are able to drive the network to a certain state. However, previous work uses a linear model of the network dynamics, and it is unclear if the real neuronal network conforms to this assumption. Here, we propose a method to build a global, low-dimensional model of the dynamics, whereby an underlying global linear dynamical system is actuated by temporally sparse control signals. A key novelty of this method is discovering candidate control signals that the network uses to control itself. We analyse these control signals in two ways, showing they are interpretable and biologically plausible. First, these control signals are associated with transitions between behaviours, which were previously annotated via expert-generated features. Second, these signals can be predicted both from neurons previously implicated in behavioural transitions but also additional neurons previously unassociated with these behaviours. The proposed mathematical framework is generic and can be generalized to other neurosensory systems, potentially revealing transitions and their encodings in a completely unsupervised way.     

Optimal design of capacitated production networks

In this work we present a mixed-integer model for the optimal design of production/transportation systems. In contrast to standard design problems, our model is originally based on a coupled system of differential equations capturing the dynamics of manufacturing processes and stocks. The problem is to select an optimal parameter configuration from a predefined set such that respective constraints are fulfilled. We focus on single commodity flows over large time scales as well as highly interconnected networks and propose a suitable start heuristic to ensure feasibility and to speed up the solution procedure.     

Neural networks research in context: A longitudinal journal cocitation analysis of an emerging interdisciplinary field

A cocitation analysis for thirty-six journals and other publications in neural networks research and related disciplines was conducted over three consecutive time periods spanning the years 1990-early 1997. Cluster analysis and MDS maps identified groupings representing foundation research areas (physics/optics, computer engineering, neuroscience, expert systems & cognition, and perception) along with neural networks and mathematical modeling of neural systems. Principal components analysis demonstrated a similar structure, with several journals and books loading on a majority of the factors. An INDSCAL analysis showed an increasing separation between natural sciences/psychology and engineering/neural networks research from the first time period to the third.     

Evolutionary paths of change of emerging nanotechnological innovation systems: the case of ZnO nanostructures

This paper puts forward a quantitative approach aimed at the understanding of the evolutionary paths of change of emerging nanotechnological innovation systems. The empirical case of the newly emerging zinc oxide one-dimensional nanostructures is used. In line with other authors, ‘problems’ are visualized as those aspects guiding the dynamics of innovation systems. It is argued that the types of problems confronted by an innovation system, and in turn its dynamics of change, are imprinted on the nature of the underlying knowledge bases. The latter is operationalized through the construction of co-citation networks from scientific publications. We endow these co-citation networks with directionality through the allocation of a particular problem, drawn from a ‘problem space’ for nanomaterials, to each network node. By analyzing the longitudinal, structural and cognitive changes undergone by these problem-attached networks, we attempt to infer the nature of the paths of change of emerging nanotechnological innovation systems. Overall, our results stress the evolutionary mechanisms underlying change in a specific N&N subfield. It is observed that the latter may exert significant influence on the innovative potentials of nanomaterials.     

Controlling enzymatic reactions by geometry in a biomimetic nanoscale network

We demonstrate that a transition from a compact geometry (sphere) to a structured geometry (several spheres connected by nanoconduits) in nanotube-vesicle networks (NVNs) induces an ordinary enzyme-catalyzed reaction to display wavelike properties. The reaction dynamics can be controlled directly by the geometry of the network, and such networks can be used to generate wavelike patterns in product formation. The results have bearing for understanding catalytic reactions in biological systems as well as for designing emerging wet chemical nanotechnological devices.     

Proximity of intracellular regulatory networks to monotone systems

Networks that contain only sign-consistent loops, such as positive feedforward and feedback loops, function as monotone systems. Simulated using differential equations, monotone systems display well-ordered behaviour that excludes the possibility for chaotic dynamics. Perturbations of such systems have unambiguous global effects and a predictability characteristic that confers robustness and adaptability. The authors assess whether the topology of biological regulatory networks is similar to the topology of monotone systems. For this, three intracellular regulatory networks are analysed where links are specified for the directionality and the effects of interactions. These networks were assembled from functional studies in the experimental literature. It is found that the three biological networks contain far more positive 'sign-consistent' feedback and feedforward loops than negative loops. Negative loops can be 'eliminated' from the real networks by the removal of fewer links as compared with the corresponding shuffled networks. The abundance of positive feedforward and feedback loops in real networks emerges from the presence of hubs that are enriched with either negative or positive links. These observations suggest that intracellular regulatory networks are 'close-to-monotone', a characteristic that could contribute to the dynamical stability observed in cellular behaviour.     

Probabilistic patterns of interaction: the effects of link-strength variability on food web structure

Patterns of species interactions affect the dynamics of food webs. An important component of species interactions that is rarely considered with respect to food webs is the strengths of interactions, which may affect both structure and dynamics. In natural systems, these strengths are variable, and can be quantified as probability distributions. We examined how variation in strengths of interactions can be described hierarchically, and how this variation impacts the structure of species interactions in predator–prey networks, both of which are important components of ecological food webs. The stable isotope ratios of predator and prey species may be particularly useful for quantifying this variability, and we show how these data can be used to build probabilistic predator–prey networks. Moreover, the distribution of variation in strengths among interactions can be estimated from a limited number of observations. This distribution informs network structure, especially the key role of dietary specialization, which may be useful for predicting structural properties in systems that are difficult to observe. Finally, using three mammalian predator–prey networks (two African and one Canadian) quantified from stable isotope data, we show that exclusion of link-strength variability results in biased estimates of nestedness and modularity within food webs, whereas the inclusion of body size constraints only marginally increases the predictive accuracy of the isotope-based network. We find that modularity is the consequence of strong link-strengths in both African systems, while nestedness is not significantly present in any of the three predator–prey networks.     

The sleeping brain as a complex system

'Complexity science' is a rapidly developing research direction with applications in a multitude of fields that study complex systems consisting of a number of nonlinear elements with interesting dynamics and mutual interactions. This Theme Issue 'The complexity of sleep' aims at fostering the application of complexity science to sleep research, because the brain in its different sleep stages adopts different global states that express distinct activity patterns in large and complex networks of neural circuits. This introduction discusses the contributions collected in the present Theme Issue. We highlight the potential and challenges of a complex systems approach to develop an understanding of the brain in general and the sleeping brain in particular. Basically, we focus on two topics: the complex networks approach to understand the changes in the functional connectivity of the brain during sleep, and the complex dynamics of sleep, including sleep regulation. We hope that this Theme Issue will stimulate and intensify the interdisciplinary communication to advance our understanding of the complex dynamics of the brain that underlies sleep and consciousness.     

Stability in flux: community structure in dynamic networks

The structure of many biological, social and technological systems can usefully be described in terms of complex networks. Although often portrayed as fixed in time, such networks are inherently dynamic, as the edges that join nodes are cut and rewired, and nodes themselves update their states. Understanding the structure of these networks requires us to understand the dynamic processes that create, maintain and modify them. Here, we build upon existing models of coevolving networks to characterize how dynamic behaviour at the level of individual nodes generates stable aggregate behaviours. We focus particularly on the dynamics of groups of nodes formed endogenously by nodes that share similar properties (represented as node state) and demonstrate that, under certain conditions, network modularity based on state compares well with network modularity based on topology. We show that if nodes rewire their edges based on fixed node states, the network modularity reaches a stable equilibrium which we quantify analytically. Furthermore, if node state is not fixed, but can be adopted from neighbouring nodes, the distribution of group sizes reaches a dynamic equilibrium, which remains stable even as the composition and identity of the groups change. These results show that dynamic networks can maintain the stable community structure that has been observed in many social and biological systems.     

Dynamics on higher-order networks: a review

Network science has evolved into an indispensable platform for studying complex systems. But recent research has identified limits of classical networks, where links connect pairs of nodes, to comprehensively describe group interactions. Higher-order networks, where a link can connect more than two nodes, have therefore emerged as a new frontier in network science. Since group interactions are common in social, biological and technological systems, higher-order networks have recently led to important new discoveries across many fields of research. Here, we review these works, focusing in particular on the novel aspects of the dynamics that emerges on higher-order networks. We cover a variety of dynamical processes that have thus far been studied, including different synchronization phenomena, contagion processes, the evolution of cooperation and consensus formation. We also outline open challenges and promising directions for future research.     

Minimum steering node set of complex networks and its applications to biomolecular networks

Many systems of interests in practices can be represented as complex networks. For biological systems, biomolecules do not perform their functions alone but interact with each other to form so‐called biomolecular networks. A system is said to be controllable if it can be steered from any initial state to any other final state in finite time. The network controllability has become essential to study the dynamics of the networks and understand the importance of individual nodes in the networks. Some interesting biological phenomena have been discovered in terms of the structural controllability of biomolecular networks. Most of current studies investigate the structural controllability of networks in notion of the minimum driver node sets (MDSs). In this study, the authors analyse the network structural controllability in notion of the minimum steering node sets (MSSs). They first develop a graph‐theoretic algorithm to identify the MSS for a given network and then apply it to several biomolecular networks. Application results show that biomolecules identified in the MSSs play essential roles in corresponding biological processes. Furthermore, the application results indicate that the MSSs can reflect the network dynamics and node importance in controlling the networks better than the MDSs.Inspec keywords: molecular biophysics, biocontrol, graph theoryOther keywords: graph‐theoretic algorithm, MSS, minimum driver node sets, structural controllability, network dynamics, network controllability, biological systems, biomolecular networks, complex networks, minimum steering node set     

Adaptive coevolutionary networks: a review.

Adaptive networks appear in many biological applications. They combine topological evolution of the network with dynamics in the network nodes. Recently, the dynamics of adaptive networks has been investigated in a number of parallel studies from different fields, ranging from genomics to game theory. Here we review these recent developments and show that they can be viewed from a unique angle. We demonstrate that all these studies are characterized by common themes, most prominently: complex dynamics and robust topological self-organization based on simple local rules.     

Stabilization of periodic orbits near a subcritical Hopf bifurcation in delay-coupled networks

We study networks of delay-coupled oscillators with the aim to extend time-delayed feedback control to networks. We show that unstable periodic orbits of a network can be stabilized by a noninvasive, delayed coupling. We state criteria for stabilizing the orbits by delay-coupling in networks and apply these to the case where the local dynamics is close to a subcritical Hopf bifurcation, which is representative of systems with torsion-free unstable periodic orbits. Using the multiple scale method and the master stability function approach, the network system is reduced to the normal form, and the characteristic equations for Floquet exponents are derived in an analytical form, which reveals the coupling parameters for successful stabilization. Finally, we illustrate the results by numerical simulations of the Lorenz system close to a subcritical Hopf bifurcation. The unstable periodic orbits in this system have no torsion, and hence cannot be stabilized by the conventional time delayed-feedback technique.     

Entanglement dynamics and quasi-periodicity in discrete quantum walks

We study the entanglement dynamics of discrete time quantum walks acting on bounded finite sized graphs. We demonstrate that, depending on system parameters, the dynamics may be monotonic, oscillatory but highly regular, or quasi-periodic. While the dynamics of the system are not chaotic since the system comprises linear evolution, the dynamics often exhibit some features similar to chaos such as high sensitivity to the system's parameters, irregularity and infinite periodicity. Our observations are of interest for entanglement generation, which is one primary use for the quantum walk formalism. Furthermore, we show that the systems we model can easily be mapped to optical beamsplitter networks, rendering experimental observation of quasi-periodic dynamics within reach.     

Collaboration structures in Slovenian scientific communities

We combine two seemingly distinct perspectives regarding the modeling of network dynamics. One perspective is found in the work of physicists and mathematicians who formally introduced the small world model and the mechanism of preferential attachment. The other perspective is sociological and focuses on the process of cumulative advantage and considers the agency of individual actors in a network. We test hypotheses, based on work drawn from these perspectives, regarding the structure and dynamics of scientific collaboration networks. The data we use are for four scientific disciplines in the Slovene system of science. The results deal with the overall topology of these networks and specific processes that generate them. The two perspectives can be joined to mutual benefit. Within this combined approach, the presence of small-world structures was confirmed. However preferential attachment is far more complex than advocates of a single autonomous mechanism claim.     

Slow filament dynamics and viscoelasticity in entangled and active actin networks

This paper deals with correlations between the viscoelastic impedance of entangled actin networks and the slow conformational dynamics and diffusive motions of single filaments. The single filament dynamics is visualized and analysed by analysing the Brownian motion of attached colloidal beads, which enables independent measurements of characteristic viscoelastic response times such as the entanglement and reptation times. We further studied the frequency-dependent viscoelastic impedance of active actin-heavy-meromyosin II networks by magnetic-tweezers microrheometry to gain insight into the effect of such highly dynamic and force-generating crosslinkers (exhibiting bond lifetimes of less than 1 s) on the rheological properties. We show that at high frequencies (higher than 1 Hz) the viscoelastic loss modulus is slightly increased relative to the entangled network (associated with an increase in the energy dissipated during mechanical excitations), while at low frequencies the plateau of the impedance spectrum becomes more pronounced as a consequence of the cross-linking of the network and the suppression of the terminal regime. Our data provide evidence that the myosin motor protein may play a role as softener of the actin cortex, enabling the adaptive reduction of the yield stress of cells and thus facilitating cellular deformations.     

Multi-cluster dynamics in coupled phase oscillator networks

In this paper, we examine robust clustering behaviour with multiple nontrivial clusters for identically and globally coupled phase oscillators. These systems are such that the dynamics is completely determined by the number of oscillators N and a single scalar function g(?) (the coupling function). Previous work has shown that (a) any clustering can stably appear via choice of a suitable coupling function and (b) open sets of coupling functions can generate heteroclinic network attractors between cluster states of saddle type, though there seem to be no examples where saddles with more than two nontrivial clusters are involved. In this work, we clarify the relationship between the coupling function and the dynamics. We focus on cases where the clusters are inequivalent in the sense of not being related by a temporal symmetry, and demonstrate that there are coupling functions that give robust heteroclinic networks between periodic states involving three or more nontrivial clusters. We consider an example for N = 6 oscillators where the clustering is into three inequivalent clusters. We also discuss some aspects of the bifurcation structure for periodic multi-cluster states and show that the transverse stability of inequivalent clusters can, to a large extent, be varied independently of the tangential stability.     

Editorial - Selected papers from the 2nd international symposium on optimization and systems biology

One of the major challenges for post-genomic biology is to understand how genes, proteins and small molecules interact to form cellular systems. It has been recognised that a complicated living organism cannot be fully understood by merely analysing individual components, and that interactions of those components or networks are ultimately responsible for an organism?s form and functions. Instead of analysing individual components or aspects of the organism, systems biology is the study of an organism, viewed as a dynamical and interacting network of biomolecules which give rise to a complicated life. With increasingly accumulated data from high-throughput technologies, molecular networks and their dynamics have been studied extensively from various aspects of living organisms. Many mathematical methods have been adopted in computational systems biology; in particular, optimisation and statistics play a key role in analysing and understanding biological mechanisms from system-wide viewpoints.