81 research outputs found

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm

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    Online shopping is a multi-billion-dollar industry worldwide. However, several challenges related to purchase intention can impact the sales of e-commerce. For example, e-commerce platforms are unable to identify which factors contribute to the high sales of a product. Besides, online sellers have difficulty finding products that align with customers’ preferences. Therefore, this work will utilize an artificial neural network to provide knowledge extraction for the online shopping industry or e-commerce platforms that might improve their sales and services. There are limited attempts to propose knowledge extraction with neural network models in the online shopping field, especially research revolving around online shoppers’ purchasing intentions. In this study, 2-satisfiability logic was used to represent the shopping attribute and a special recurrent artificial neural network named Hopfield neural network was employed. In reducing the learning complexity, a genetic algorithm was implemented to optimize the logical rule throughout the learning phase in performing a 2-satisfiability-based reverse analysis method, implemented during the learning phase as this method was compared. The performance of the genetic algorithm with 2-satisfiability-based reverse analysis was measured according to the selected performance evaluation metrics. The simulation suggested that the proposed model outperformed the existing model in doing logic mining for the online shoppers dataset

    Frontiers in psychodynamic neuroscience

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    he term psychodynamics was introduced in 1874 by Ernst von Brücke, the renowned German physiologist and Freud’s research supervisor at the University of Vienna. Together with Helmholtz and others, Brücke proposed that all living organisms are energy systems, regulated by the same thermodynamic laws. Since Freud was a student of Brücke and a deep admirer of Helmholtz, he adopted this view, thus laying the foundations for his metapsychology. The discovery of the Default Network and the birth of Neuropsychoanalysis, twenty years ago, facilitated a deep return to this classical conception of the brain as an energy system, and therefore a return to Freud's early ambition to establish psychology as natural science. Our current investigations of neural networks and applications of the Free Energy Principle are equally ‘psychodynamic’ in Brücke’s original sense of the term. Some branches of contemporary neuroscience still eschew subjective data and therefore exclude the brain’s most remarkable property – its selfhood – from the field, and many neuroscientists remain skeptical about psychoanalytic methods, theories, and concepts. Likewise, some psychoanalysts continue to reject any consideration of the structure and functions of the brain from their conceptualization of the mind in health and disease. Both cases seem to perpetuate a Cartesian attitude in which the mind is linked to the brain in some equivocal relationship and an attitude that detaches the brain from the body -- rather than considering it an integral part of the complex and dynamic living organism as a whole. Evidence from psychodynamic neuroscience suggests that Freudian constructs can now be realized neurobiologically. For example, Freud’s notion of primary and secondary processes is consistent with the hierarchical organization of self-organized cortical and subcortical systems, and his description of the ego is consistent with the functions of the Default Network and its reciprocal exchanges with subordinate brain systems. Moreover, thanks to new methods of measuring brain entropy, we can now operationalize the primary and secondary processes and therefore test predictions arising from these Freudian constructs. All of this makes it possible to deepen the dialogue between neuroscience and psychoanalysis, in ways and to a degree that was unimaginable in Freud's time, and even compared to twenty years ago. Many psychoanalytical hypotheses are now well integrated with contemporary neuroscience. Other Freudian and post-Freudian hypotheses about the structure and function of the mind seem ripe for the detailed and sophisticated development that modern psychodynamic neuroscience can offer. This Research Topic aims to provide comprehensive coverage of the latest advances in psychodynamic neuroscience and neuropsychoanalysis. Potential authors are invited to submit papers (original research, case reports, review articles, commentaries) that deploy, review, compare or develop the methods and theories of psychodynamic neuroscience and neuropsychoanalysis. Potential authors include researchers, psychoanalysts, and neuroscientists

    Machine Learning Methods with Noisy, Incomplete or Small Datasets

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    In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

    Using Active Learning to Teach Critical and Contextual Studies: One Teaching Plan, Two Experiments, Three Videos.

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    Since the 1970s, art and design education at UK universities has existedas a divided practice; on the one hand applying active learning in thestudio and on the other hand enforcing passive learning in the lecturetheatre. As a result, art and design students are in their vast majorityreluctant about modules that may require them to think, read and writecritically during their academic studies. This article describes, evaluatesand analyses two individual active learning experiments designed todetermine if it is possible to teach CCS modules in a manner thatencourages student participation. The results reveal that opting foractive learning methods improved academic achievement, encouragedcooperation, and enforced an inclusive classroom. Furthermore, andcontrary to wider perception, the article demonstrates that activelearning methods can be equally beneficial for small-size as well aslarge-size groups

    Practical approaches to delivering pandemic impacted laboratory teaching

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    #DryLabsRealScience is a community of practice established to support life science educators with the provision of laboratory-based classes in the face of the COVID-19 pandemic and restricted access to facilities. Four key approaches have emerged from the innovative work shared with the network: videos, simulations, virtual/augmented reality, and datasets, with each having strengths and weaknesses. Each strategy was used pre-COVID and has a sound theoretical underpinning; here, we explore how the pandemic has forced their adaptation and highlight novel utilisation to support student learning in the laboratory environment during the challenges faced by remote and blended teaching

    Computer Aided Verification

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    This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

    31th International Conference on Information Modelling and Knowledge Bases

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    Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

    Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

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    Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202
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