Memory and Epileptogenesis in Complex Biological and Simulated Systems

Oscillations of neural activity may bind widespread cortical areas into a neural representation that encodes disparate aspects of an event. In order to test this theory we have turned to data collected from complex partial epilepsy (CPE) patients with chronically implanted depth electrodes. Data from regions critical to word and face information processing was analyzed using spectral coherence measurements. Similar analyses of intracranial EEG (iEEG) during seizure episodes display HippoCampal Formation (HCF)—NeoCortical (NC) spectral coherence patterns that are characteristic of specific seizure stages (Klopp et al. 1996). We are now building a computational memory model to examine whether spatio-temporal patterns of human iEEG spectral coherence emerge in a computer simulation of HCF cellular distribution, membrane physiology and synaptic connectivity. Once the model is reasonably scaled it will be used as a tool to explore neural parameters that are critical to memory formation and epileptogenesis.

Bayesian networks for information synthesis in complex systems

This thesis introduces a method of applying Bayesian Networks to combine information from a range of data sources for effective decision support systems. It develops a set of techniques in development, validation, visualisation, and application of Complex Systems models, with a working demonstration in an Australian airport environment. The methods presented here have provided a modelling approach that produces highly flexible, informative and applicable interpretations of a system's behaviour under uncertain conditions. These end-to-end techniques are applied to the development of model based dashboards to support operators and decision makers in the multi-stakeholder airport environment. They provide highly flexible and informative interpretations and confidence in these interpretations of a system's behaviour under uncertain conditions.

Towards adaptive management of QoS-aware service compositions - Functional architecture

Service compositions enable users to realize their complex needs as a single request. Despite intensive research, especially in the area of business processes, web services and grids, an open and valid question is still how to manage service compositions in order to satisfy both functional and non-functional requirements as well as adapt to dynamic changes. In this paper we propose an (functional) architecture for adaptive management of QoS-aware service compositions. Comparing to the other existing architectures this one offers two major advantages. Firstly, this architecture supports various execution strategies based on dynamic selection and negotiation of services included in a service composition, contracting based on service level agreements, service enactment with flexible support for exception handling, monitoring of service level objectives, and profiling of execution data. Secondly, the architecture is built on the basis of well know existing standards to communicate and exchange data, which significantly reduces effort to integrate existing solutions and tools from different vendors. A first prototype of this architecture has been implemented within an EU-funded Adaptive Service Grid project. © 2006 Springer-Verlag.

Dynamic Bayesian network inferencing for non-homogeneous complex systems

Dynamic Bayesian Networks (DBNs) provide a versatile platform for predicting and analysing the behaviour of complex systems. As such, they are well suited to the prediction of complex ecosystem population trajectories under anthropogenic disturbances such as the dredging of marine seagrass ecosystems. However, DBNs assume a homogeneous Markov chain whereas a key characteristics of complex ecosystems is the presence of feedback loops, path dependencies and regime changes whereby the behaviour of the system can vary based on past states. This paper develops a method based on the small world structure of complex systems networks to modularise a non-homogeneous DBN and enable the computation of posterior marginal probabilities given evidence in forwards inference. It also provides an approach for an approximate solution for backwards inference as convergence is not guaranteed for a path dependent system. When applied to the seagrass dredging problem, the incorporation of path dependency can implement conditional absorption and allows release from the zero state in line with environmental and ecological observations. As dredging has a marked global impact on seagrass and other marine ecosystems of high environmental and economic value, using such a complex systems model to develop practical ways to meet the needs of conservation and industry through enhancing resistance and/or recovery is of paramount importance.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.

IMIA working group on health record banking - How complex systems can cooperate towards having individual's consolidated data

This workshop aims at discussing alternative approaches to resolving the problem of health information fragmentation, partially resulting from difficulties of health complex systems to semantically interact at the information level. In principle, we challenge the current paradigm of keeping medical records where they were created and discuss an alternative approach in which an individual's health data can be maintained by new entities whose sole responsibility is the sustainability of individual-centric health records. In particular, we will discuss the unique characteristics of the European health information landscape. This workshop is also a business meeting of the IMIA Working Group on Health Record Banking.