Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.

Stationary scattering from a nonlinear network

Transmission through a complex network of nonlinear one-dimensional leads is discussed by extending the stationary scattering theory on quantum graphs to the nonlinear regime. We show that the existence of cycles inside the graph leads to a large number of sharp resonances that dominate scattering. The latter resonances are then shown to be extremely sensitive to the nonlinearity and display multistability and hysteresis. This work provides a framework for the study of light propagation in complex optical networks.

The interplay between microscopic and mesoscopic structures in complex networks

Understanding a complex network's structure holds the key to understanding its function. The physics community has contributed a multitude of methods and analyses to this cross-disciplinary endeavor. Structural features exist on both the microscopic level, resulting from differences between single node properties, and the mesoscopic level resulting from properties shared by groups of nodes. Disentangling the determinants of network structure on these different scales has remained a major, and so far unsolved, challenge. Here we show how multiscale generative probabilistic exponential random graph models combined with efficient, distributive message-passing inference techniques can be used to achieve this separation of scales, leading to improved detection accuracy of latent classes as demonstrated on benchmark problems. It sheds new light on the statistical significance of motif-distributions in neural networks and improves the link-prediction accuracy as exemplified for gene-disease associations in the highly consequential Online Mendelian Inheritance in Man database. © 2011 Reichardt et al.

Communities and emerging semantics in semantic link network:discovery and learning

The World Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first suggests the Semantic Link Network (SLN), a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested.

Suppression and summation in contrast gain control for human vision

Over the last ten years our understanding of early spatial vision has improved enormously. The long-standing model of probability summation amongst multiple independent mechanisms with static output nonlinearities responsible for masking is obsolete. It has been replaced by a much more complex network of additive, suppressive, and facilitatory interactions and nonlinearities across eyes, area, spatial frequency, and orientation that extend well beyond the classical recep-tive field (CRF). A review of a substantial body of psychophysical work performed by ourselves (20 papers), and others, leads us to the following tentative account of the processing path for signal contrast. The first suppression stage is monocular, isotropic, non-adaptable, accelerates with RMS contrast, most potent for low spatial and high temporal frequencies, and extends slightly beyond the CRF. Second and third stages of suppression are difficult to disentangle but are possibly pre- and post-binocular summation, and involve components that are scale invariant, isotropic, anisotropic, chromatic, achromatic, adaptable, interocular, substantially larger than the CRF, and saturated by contrast. The monocular excitatory pathways begin with half-wave rectification, followed by a preliminary stage of half-binocular summation, a square-law transducer, full binocular summation, pooling over phase, cross-mechanism facilitatory interactions, additive noise, linear summation over area, and a slightly uncertain decision-maker. The purpose of each of these interactions is far from clear, but the system benefits from area and binocular summation of weak contrast signals as well as area and ocularity invariances above threshold (a herd of zebras doesn't change its contrast when it increases in number or when you close one eye). One of many remaining challenges is to determine the stage or stages of spatial tuning in the excitatory pathway.

Case study as a research strategy : investigating extreme weather resilience of construction SMEs in the UK

Determining an appropriate research methodology is considered as an important element in a research study; especially in a doctoral research study. It involves approach to the entire process of a research study, starting from theoretical underpinnings and spanning to data collection and analysis, and extending to developing the solutions for the problems investigated. Research methodology in essence is focused around the problems to be investigated in a research study and therefore varies according to the problems investigated. Thus, identifying the research methodology that best suits a research in hand is important, not only as it will benefit achieving the set objectives of a research, but also as it will serve establishing the credibility of the work. Research philosophy, approach, strategy, choice, and techniques are inherent components of the methodology. Research strategy provides the overall direction of the research including the process by which the research is conducted. Case study, experiment, survey, action research, grounded theory and ethnography are examples for such research strategies. Case study is documented as an empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident. Case study was adopted as the overarching research strategy, in a doctoral study developed to investigate the resilience of construction Small and Medium-sized Enterprises (SMEs) in the UK to extreme weather events. The research sought to investigate how construction SMEs are affected by EWEs, respond to the risk of EWEs, and means of enhancing their resilience to future EWEs. It is argued that utilising case study strategy will benefit the research study, in achieving the set objectives of the research and answering the research questions raised, by comparing and contrasting with the alternative strategies available. It is also claimed that the selected strategy will contribute towards addressing the call for improved methodological pluralism in construction management research, enhancing the understanding of complex network of relationships pertinent to the industry and the phenomenon being studied.

Case study as a research strategy:investigating extreme weather resilience of construction SMEs in the UK

Determining an appropriate research methodology is considered as an important element in a research study; especially in a doctoral research study. It involves approach to the entire process of a research study, starting from theoretical underpinnings and spanning to data collection and analysis, and extending to developing the solutions for the problems investigated. Research methodology in essence is focused around the problems to be investigated in a research study and therefore varies according to the problems investigated. Thus, identifying the research methodology that best suits a research in hand is important, not only as it will benefit achieving the set objectives of a research, but also as it will serve establishing the credibility of the work. Research philosophy, approach, strategy, choice, and techniques are inherent components of the methodology. Research strategy provides the overall direction of the research including the process by which the research is conducted. Case study, experiment, survey, action research, grounded theory and ethnography are examples for such research strategies. Case study is documented as an empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident. Case study was adopted as the overarching research strategy, in a doctoral study developed to investigate the resilience of construction Small and Medium-sized Enterprises (SMEs) in the UK to extreme weather events. The research sought to investigate how construction SMEs are affected by EWEs, respond to the risk of EWEs, and means of enhancing their resilience to future EWEs. It is argued that utilising case study strategy will benefit the research study, in achieving the set objectives of the research and answering the research questions raised, by comparing and contrasting with the alternative strategies available. It is also claimed that the selected strategy will contribute towards addressing the call for improved methodological pluralism in construction management research, enhancing the understanding of complex network of relationships pertinent to the industry and the phenomenon being studied.

Reporting and managing risk:a look at current practice at Tesco, RBS, local and central government

These case studies from CIMA highlight the need to embed risk management within more easily understood behaviours, consistent with the overall organisational culture. In each case, some form of internal audit team provides either an oversight function or acts as an expert link in that feedback loop. Frontline staff, managers and specialists should be completely aligned on risk, in part just to ensure that there is a consistency of approach. They should understand instinctively that good performance includes good risk management. Tesco has continued to thrive during the recession and remains a robust and efficient group of businesses despite the emergence of potential threats around consumer spending and the supply chain. RBS, by contrast, has suffered catastrophic and very public failures of risk management despite a large in-house function and stiff regulation of risk controls. Birmingham City Council, like all local authorities, is adapting to more commercial modes of operation and is facing diverse threats and opportunities emerging as a result of social change. And DCMS, like many other public sector organisations, has to handle an incredibly complex network of delivery partners within the context of a relatively recent overhaul of central government risk management processes. Key Findings: •Risk management is no longer solely a financial discipline, nor is it simply a concern for the internal control function. •Where organisations retain a discrete risk management cadre – often specialists at monitoring and evaluating a range of risks – their success is dependent on embedding risk awareness in the wider culture of the enterprise. •Risk management is most successful when it is explicitly linked to operational performance. •Clear leadership, specific goals, excellent influencing skills and open-mindedness to potential threats and opportunities are essential for effective risk management. •Bureaucratic processes and systems can hamper good risk management – either as a result of a ‘box-ticking mentality’ or because managers and staff believe they do not need to consider risk themselves.

The retromer coat complex coordinates endosomal sorting and dynein-mediated transport, with carrier recognition by the trans-Golgi network

Early endosome-to-trans-Golgi network (TGN) transport is organized by the retromer complex. Consisting of cargo-selective and membrane-bound subcomplexes, retromer coordinates sorting with membrane deformation and carrier formation. Here, we describe four mammalian retromers whose membrane-bound subcomplexes contain specific combinations of the sorting nexins (SNX), SNX1, SNX2, SNX5, and SNX6. We establish that retromer requires a dynamic spatial organization of the endosomal network, which is regulated through association of SNX5/SNX6 with the p150(glued) component of dynactin, an activator of the minus-end directed microtubule motor dynein; an association further defined through genetic studies in C. elegans. Finally, we also establish that the spatial organization of the retromer pathway is mediated through the association of SNX1 with the proposed TGN-localized tether Rab6-interacting protein-1. These interactions describe fundamental steps in retromer-mediated transport and establish that the spatial organization of the retromer network is a critical element required for efficient retromer-mediated sorting.

Description and training of neural network dynamics

Attractor properties of a popular discrete-time neural network model are illustrated through numerical simulations. The most complex dynamics is found to occur within particular ranges of parameters controlling the symmetry and magnitude of the weight matrix. A small network model is observed to produce fixed points, limit cycles, mode-locking, the Ruelle-Takens route to chaos, and the period-doubling route to chaos. Training algorithms for tuning this dynamical behaviour are discussed. Training can be an easy or difficult task, depending whether the problem requires the use of temporal information distributed over long time intervals. Such problems require training algorithms which can handle hidden nodes. The most prominent of these algorithms, back propagation through time, solves the temporal credit assignment problem in a way which can work only if the relevant information is distributed locally in time. The Moving Targets algorithm works for the more general case, but is computationally intensive, and prone to local minima.

Neural network control of a gas turbine

In this paper we explore the practical use of neural networks for controlling complex non-linear systems. The system used to demonstrate this approach is a simulation of a gas turbine engine typical of those used to power commercial aircraft. The novelty of the work lies in the requirement for multiple controllers which are used to maintain system variables in safe operating regions as well as governing the engine thrust.

Neural network enhanced self tuning adaptive control application for non-linear control of dynamic systems

The main theme of research of this project concerns the study of neutral networks to control uncertain and non-linear control systems. This involves the control of continuous time, discrete time, hybrid and stochastic systems with input, state or output constraints by ensuring good performances. A great part of this project is devoted to the opening of frontiers between several mathematical and engineering approaches in order to tackle complex but very common non-linear control problems. The objectives are: 1. Design and develop procedures for neutral network enhanced self-tuning adaptive non-linear control systems; 2. To design, as a general procedure, neural network generalised minimum variance self-tuning controller for non-linear dynamic plants (Integration of neural network mapping with generalised minimum variance self-tuning controller strategies); 3. To develop a software package to evaluate control system performances using Matlab, Simulink and Neural Network toolbox. An adaptive control algorithm utilising a recurrent network as a model of a partial unknown non-linear plant with unmeasurable state is proposed. Appropriately, it appears that structured recurrent neural networks can provide conveniently parameterised dynamic models for many non-linear systems for use in adaptive control. Properties of static neural networks, which enabled successful design of stable adaptive control in the state feedback case, are also identified. A survey of the existing results is presented which puts them in a systematic framework showing their relation to classical self-tuning adaptive control application of neural control to a SISO/MIMO control. Simulation results demonstrate that the self-tuning design methods may be practically applicable to a reasonably large class of unknown linear and non-linear dynamic control systems.

Building cliques and alliances as practices to 'make things happen' in complex networks

The Roma population has become a policy issue highly debated in the European Union (EU). The EU acknowledges that this ethnic minority faces extreme poverty and complex social and economic problems. 52% of the Roma population live in extreme poverty, 75% in poverty (Soros Foundation, 2007, p. 8), with a life expectancy at birth of about ten years less than the majority population. As a result, Romania has received a great deal of policy attention and EU funding, being eligible for 19.7 billion Euros from the EU for 2007-2013. Yet progress is slow; it is debated whether Romania's government and companies were capable to use these funds (EurActiv.ro, 2012). Analysing three case studies, this research looks at policy implementation in relation to the role of Roma networks in different geographical regions of Romania. It gives insights about how to get things done in complex settings and it explains responses to the Roma problem as a „wicked‟ policy issue. This longitudinal research was conducted between 2008 and 2011, comprising 86 semi-structured interviews, 15 observations, and documentary sources and using a purposive sample focused on institutions responsible for implementing social policies for Roma: Public Health Departments, School Inspectorates, City Halls, Prefectures, and NGOs. Respondents included: governmental workers, academics, Roma school mediators, Roma health mediators, Roma experts, Roma Councillors, NGOs workers, and Roma service users. By triangulating the data collected with various methods and applied to various categories of respondents, a comprehensive and precise representation of Roma network practices was created. The provisions of the 2001 „Governmental Strategy to Improve the Situation of the Roma Population‟ facilitated forming a Roma network by introducing special jobs in local and central administration. In different counties, resources, people, their skills, and practices varied. As opposed to the communist period, a new Roma elite emerged: social entrepreneurs set the pace of change by creating either closed cliques or open alliances and by using more or less transparent practices. This research deploys the concept of social/institutional entrepreneurs to analyse how key actors influence clique and alliance formation and functioning. Significantly, by contrasting three case studies, it shows that both closed cliques and open alliances help to achieve public policy network objectives, but that closed cliques can also lead to failure to improve the health and education of Roma people in a certain region.

BMI not WHR modulates BOLD fMRI responses in a sub-cortical reward network when participants judge the attractiveness of human female bodies

In perceptual terms, the human body is a complex 3d shape which has to be interpreted by the observer to judge its attractiveness. Both body mass and shape have been suggested as strong predictors of female attractiveness. Normally body mass and shape co-vary, and it is difficult to differentiate their separate effects. A recent study suggested that altering body mass does not modulate activity in the reward mechanisms of the brain, but shape does. However, using computer generated female body-shaped greyscale images, based on a Principal Component Analysis of female bodies, we were able to construct images which covary with real female body mass (indexed with BMI) and not with body shape (indexed with WHR), and vice versa. Twelve observers (6 male and 6 female) rated these images for attractiveness during an fMRI study. The attractiveness ratings were correlated with changes in BMI and not WHR. Our primary fMRI results demonstrated that in addition to activation in higher visual areas (such as the extrastriate body area), changing BMI also modulated activity in the caudate nucleus, and other parts of the brain reward system. This shows that BMI, not WHR, modulates reward mechanisms in the brain and we infer that this may have important implications for judgements of ideal body size in eating disordered individuals.