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Книги з теми "Computational neuroimaging"

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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Li, Ping, and Hua Shu. Language and the brain: computational and neuroimaging evidence from Chinese. Oxford University Press, 2010. http://dx.doi.org/10.1093/oxfordhb/9780199541850.013.0007.

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Tang, Xiaoying, Thomas Fletcher, and Michael I. Miller, eds. Bayesian Estimation and Inference in Computational Anatomy and Neuroimaging: Methods & Applications. Frontiers Media SA, 2019. http://dx.doi.org/10.3389/978-2-88945-984-1.

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Sensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.98172.

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Adams, Reginald B., Daniel N. Albohn, and Kestutis Kveraga. A Social Vision Account of Facial Expression Perception. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190613501.003.0017.

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In this chapter, we discuss prospects for a future computational neuropsychology. Computerized approaches to assessment, the ability to implement life-like scenarios in a controlled virtual environment, and teleneuropsychology offer promise for expanding available approaches to cognitive remediation and self-monitoring. Computational models are also available increasingly for integrating neuroimaging into the assessment process. Neuropsychologists can use neuroimaging to develop new frameworks for neuropsychological testing that are rooted in the current evidence base on large-scale brain system interactions. This will allow for traditional assessment of discrete areas of neurocognitive functioning to be brought in line with recent findings that highly nuanced relations exist among brain networks. Furthermore, the new findings from systems neuroscience may allow for the development of neuropsychological assessments with greater accuracy and increased targeted testing. Neuroinfomatic approaches offer computational neuropsychology an approach to knowledge sharing via well-defined neuropsychological ontologies and collaborative knowledgebases.

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Chung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.

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Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.

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Chung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.

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Chung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.

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Roggeman, Chantal, Wim Fias, and Tom Verguts. Basic Number Representation and Beyond. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.68.

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We discuss recent computational network models of elementary number processing. One key issue to emerge from this work is a crucial distinction between symbolic and non-symbolic number representation, and the related distinction between number-selective and number-sensitive coding. Empirical predictions from the models were tested, and are here summarized. Another issue is the relation with task-based decision making mechanisms. In both lab and real-life settings, representations are seldomly accessed in a task-neutral manner, rather subjects are usually presented with a task. A related theme is the functional association between number representations and working memory. In these issues also, both modeling and neuroimaging work is summarized. To conclude, we propose that the combined modeling-neuroimaging approach should be extended to tackle more complex questions about number processing (e.g. fractions, development, dyscalculia).

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Shaikh, Mohd Faraz. Machine Learning in Detecting Auditory Sequences in Magnetoencephalography Data : Research Project in Computational Modelling and Simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.25368/2022.411.

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Does your brain replay your recent life experiences while you are resting? An open question in neuroscience is which events does our brain replay and is there any correlation between the replay and duration of the event? In this study I tried to investigate this question by using Magnetoencephalography data from an active listening experiment. Magnetoencephalography (MEG) is a non-invasive neuroimaging technique used to study the brain activity and understand brain dynamics in perception and cognitive tasks particularly in the fields of speech and hearing. It records the magnetic field generated in our brains to detect the brain activity. I build a machine learning pipeline which uses part of the experiment data to learn the sound patterns and then predicts the presence of sound in the later part of the recordings in which the participants were made to sit idle and no sound was fed. The aim of the study of test replay of learned sound sequences in the post listening period. I have used classification scheme to identify patterns if MEG responses to different sound sequences in the post task period. The study concluded that the sound sequences can be identified and distinguished above theoretical chance level and hence proved the validity of our classifier. Further, the classifier could predict the sound sequences in the post-listening period with very high probability but in order to validate the model results on post listening period, more evidence is needed.

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Novick, Jared M., Michael F. Bunting, Michael R. Dougherty, and Randall W. Engle, eds. Cognitive and Working Memory Training. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780199974467.001.0001.

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We have assembled an interdisciplinary group of distinguished authors—all experts in the field—who have been testing the efficacy of cognitive and working-memory training using a combination of behavioral, neuroimaging, meta-analytic, and computational modelling methods. This edited volume is a defining resource on the practicality, utility, and validity of the field of cognitive training research in general, and working memory training in particular. Importantly, one focus of the book is on the notion of transfer-namely, the extent to which cognitive training generalizes to learning and performance measures that were decidedly not part of the training regimen.

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Operskalski, Joachim T., and Aron K. Barbey. Cognitive Neuroscience of Causal Reasoning. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.16.

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The era of functional neuroimaging promised to shed light on dark corners of the brain’s inner workings, breathing new life into subfields of psychology beset by controversy. Although revelations from neuroscience provide the foundation for current views on many aspects of human cognition, there continue to be areas of study in which a mismatch between the questions asked by psychologists and neuroscientists renders the implications of neuroscience research unclear. Causal reasoning is one such topic, for which decades of cognitive neuroscience findings have revealed a heterogeneity of participating brain regions and networks across different experimental paradigms. This chapter discusses (i) three cognitive and computational models of causal reasoning (mental models, causal models, and force composition theory), (ii) experimental findings on causal judgment and reasoning using cognitive neuroscience methods, and (iii) the need for a multidisciplinary approach to understanding the nature and mechanisms of causal reasoning.

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Hagoort, Peter, ed. Human Language. The MIT Press, 2019. http://dx.doi.org/10.7551/mitpress/10841.001.0001.

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A unique overview of the human language faculty at all levels of organization. Language is not only one of the most complex cognitive functions that we command, it is also the aspect of the mind that makes us uniquely human. Research suggests that the human brain exhibits a language readiness not found in the brains of other species. This volume brings together contributions from a range of fields to examine humans' language capacity from multiple perspectives, analyzing it at genetic, neurobiological, psychological, and linguistic levels. In recent decades, advances in computational modeling, neuroimaging, and genetic sequencing have made possible new approaches to the study of language, and the contributors draw on these developments. The book examines cognitive architectures, investigating the functional organization of the major language skills; learning and development trajectories, summarizing the current understanding of the steps and neurocognitive mechanisms in language processing; evolutionary and other preconditions for communication by means of natural language; computational tools for modeling language; cognitive neuroscientific methods that allow observations of the human brain in action, including fMRI, EEG/MEG, and others; the neural infrastructure of language capacity; the genome's role in building and maintaining the language-ready brain; and insights from studying such language-relevant behaviors in nonhuman animals as birdsong and primate vocalization. Section editorsChristian F. Beckmann, Carel ten Cate, Simon E. Fisher, Peter Hagoort, Evan Kidd, Stephen C. Levinson, James M. McQueen, Antje S. Meyer, David Poeppel, Caroline F. Rowland, Constance Scharff, Ivan Toni, Willem Zuidema

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Logie, Robert, Valerie Camos, and Nelson Cowan, eds. Working Memory. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198842286.001.0001.

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Working memory refers to how we keep track of what we are doing moment to moment throughout our waking lives. It allows us to remember what we have just done, focus on what we are doing now, to solve problems, be creative, think about what we will be doing in the next few seconds, and continually to update in our mind changes around us throughout the day. This book brings together in one volume, state-of-the-science chapters written by some of the most productive and well-known working memory researchers worldwide. Chapters cover leading-edge research on working memory, using behavioural experimental techniques, neuroimaging, computational modelling, development across the healthy human lifespan, and studies of neurodegenerative disease and focal brain damage. A unique feature of the book is that each chapter starts with answers to a set of common questions for all authors. This allows readers very rapidly to compare key differences in theoretical assumptions and approaches to working memory across chapters, and to understand the theoretical context before going on to read each chapter in detail. All authors also have been asked to consider evidence that is not consistent with their theoretical assumptions. It is very common for authors to ignore contradictory evidence. This approach has led to new interpretations and new hypotheses for future research to greatly enhance our understanding of this crucial human ability.

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Cohen Kadosh, Roi, and Ann Dowker, eds. The Oxford Handbook of Numerical Cognition. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.001.0001.

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This book provides a comprehensive overview of numerical cognition by bringing together writing by leading researchers in psychology, neuroscience, and education, covering work using different methodological approaches in humans and animals. During the last decade there had been an explosion of studies and new findings with theoretical and translational implications. This progress has been made thanks to technological advances enabling sophisticated human neuroimaging techniques and neurophysiological studies of monkeys, and to advances in more traditional psychological and educational research. This has resulted in an enormous advance in our understanding of the neural and cognitive mechanisms of numerical cognition. In addition, there has recently been increasing interest and concern about pupils' mathematical achievement, resulting in attempts to use research to guide mathematics instruction in schools, and to develop interventions for children with mathematical difficulties. This book aims to provide a broad and extensive review of the field of numerical cognition, bringing together work from varied areas. The book covers research on important aspects of numerical cognition, involving findings from the areas of developmental psychology, cognitive psychology, human and animal neuroscience, computational modeling, neuropsychology and rehabilitation, learning disabilities education and individual differences, cross-cultural and cross-linguistic studies, and philosophy. It also includes an overview 'navigator' chapter for each section to provide a brief up-to-date review of the current literature, and to introduce and integrate the topics of the chapters in the section.

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