30 research outputs found

    26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

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    This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

    The role of modelling and computer simulations at various levels of brain organisation

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    Computational modelling and simulations are critical analytical tools in contemporary neuroscience. Models at various levels of abstraction, corresponding to levels of organisation of the brain, attempt to capture different neuronal or cognitive phenomena. This thesis discusses several models and applies them to behavioural and electrophysiological data. First, we model a voluntary decision process in a task where two available options carry the same probability of a reward for the outcome. Trial-by-trial accumulation rates are modulated by single-trial EEG features. Hierarchical Bayesian parameter estimation shows that the probability of reward is associated with changes in the speed of accumulation of evidence. Second, we use a pairwise Maximum Entropy Model (pMEM) to quantify irregularities in the MEG resting-state networks between juvenile myoclonic epilepsy (JME) patients and healthy controls. The JME group exhibited on average fewer local minima of the pMEM energy landscape than controls in the fronto-parietal network. Our results show the pMEM to be descriptive, generative model for characterising atypical functional network properties in brain disorders. Next, we use a hierarchical drift-diffusion model (HDDM) to study the integration of information from multiple sources. We observe a non-perfect integration in the case of the accumulation of both congruent and incongruent evidence. Based on fitting the HDDM parameters, we hypothesise about the neuronal implementation by extending a biologically plausible neuronal mass model of decision making. Finally, we propose a spiking neuron model that unifies various components of inferential decision-making systems. The model includes populations corresponding to anatomical regions, e.g. the dorsolateral prefrontal cortex, orbitofrontal cortex, and basal ganglia. It consists of 8000 neurons and realises dedicated cognitive operations such as weighted valuation of inputs, competition between potential actions, and urgency-mediated modulation. Overall, this work paves the way for closer integration of theoretical models with behavioural and neuroimaging dat

    Geodetic infrastructure of Serbia

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    Geodetic reference systems and their realization at the territory of Serbia have been created and maintained since the end of 19th century. Until mid-80s a series of reference geodetic networks were established: trigonometric networks in four orders, two levelling networks of high accuracybut also a series of gravimetric networks. In the following period of 20 years, there were not any organized worksaiming to maintenance of existing networks and creating new ones. In 1996, works started again on developing a new geodetic infrastructure in the form of realizing: a passive geodetic network, a network of permanent stations (AGROS – the active geodetic reference network of Serbia) as well as basic gravimetric networks. In this paperwork, a short review of works aiming to establish and use said networks is given but also a series of suggestions for a future development of geodetic infrastructure of Serbia

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    Technologies and Applications for Big Data Value

    Get PDF
    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    Breaking Down the Barriers To Operator Workload Estimation: Advancing Algorithmic Handling of Temporal Non-Stationarity and Cross-Participant Differences for EEG Analysis Using Deep Learning

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    This research focuses on two barriers to using EEG data for workload assessment: day-to-day variability, and cross- participant applicability. Several signal processing techniques and deep learning approaches are evaluated in multi-task environments. These methods account for temporal, spatial, and frequential data dependencies. Variance of frequency- domain power distributions for cross-day workload classification is statistically significant. Skewness and kurtosis are not significant in an environment absent workload transitions, but are salient with transitions present. LSTMs improve day- to-day feature stationarity, decreasing error by 59% compared to previous best results. A multi-path convolutional recurrent model using bi-directional, residual recurrent layers significantly increases predictive accuracy and decreases cross-participant variance. Deep learning regression approaches are applied to a multi-task environment with workload transitions. Accounting for temporal dependence significantly reduces error and increases correlation compared to baselines. Visualization techniques for LSTM feature saliency are developed to understand EEG analysis model biases

    29th Annual Computational Neuroscience Meeting: CNS*2020

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    Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202

    Knowledge Processes and their Role in Innovation - A Comparison of Selected Chinese and Indian Practices

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    Innovation is today recognized as key to fostering economic development and building technological strengths in firms, industries and countries. While generally described in the common understanding as anything that is new and has an impact on a large scale, it is technology-driven innovation that has assumed prominence in the contemporary environment. Academic research and study of innovation has encompassed a variety of disciplines. From these efforts, innovation has emerged as a complex phenomenon that requires a variety of factors and concepts to describe. As innovation assumes prominence in countries such as India and China, which are aiming to catch up with the more advanced countries, the factors that go to make successful innovations possible are of increasing interest. This thesis examines the different approaches adopted in the field of innovation studies and identifies knowledge processes as key to understanding innovation. The applicability of this has been investigated through detailed research into three industry segments. Based on the research, frameworks of innovation based on knowledge processes have been presented including a comparison of practices in selected Chinese and Indian organizations
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