90 research outputs found

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Analysing time-consciousness: a new account of the experienced present

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    This thesis presents a novel theory of temporal experience. While time as measured by the clock is a perennially popular topic, the time of experience remains relatively neglected and poorly understood despite its centrality to our existence. This thesis therefore sets out to address the following questions: 1) How should we characterize experiential time and the experienced present? 2) How might such distinctively temporal experience arise in the first place? While the first of these is a “what is it like” question, and thus in the realm of phenomenological analysis, the second is ostensibly closer to the realm of scientific inquiry in that it concerns an empirical phenomenon. The goal of this thesis is to synthesize plausible answers to both of these questions. As to the first question, there is already a lively debate ongoing in the philosophy of mind between various camps, each offering what they see as the most plausible analysis of the phenomenology of time. Regarding the second, there is an interdisciplinary investigation of mechanisms at various levels of abstraction, notably the levels of neurophysiology and information processing. This thesis proposes, among other contentions, that the time of experience is best characterized by a combination of retentional and extensional phenomenological analyses. This means the actual temporal extension of experience and its correlates determines, to some degree, the felt temporal character of experience. At the same time, perhaps the greater part of temporal phenomenology is determined by content: mental processes can be about temporal properties in addition to their primary contents and the way they are so partially determines the temporal character of experience as well. This analysis dovetails nicely with contemporary theories of consciousness that maintain the brain is fundamentally a complex predictive engine actively and adaptively guiding behavior. The thesis thus represents a multi-level framework for understanding the experience of time and the mechanisms underpinning it. It is my hope this perspective advances the debate on time-consciousness and can guide further work towards understanding this most fundamental aspect of our lives

    25th Annual Computational Neuroscience Meeting: CNS-2016

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    Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

    Models, Simulations, and the Reduction of Complexity

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    Modern science is a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play? To address these questions which are highly relevant to scientists as well as to philosophers of science, 8 leading natural, engineering and social scientists reflect upon their modeling work, and 8 philosophers provide a commentary

    Talent Identification and Development in Sports Performance

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    The identification and development of talent have always been a relevant topic in sports performance. In fact, a significant body of research is available worldwide discussing this longitudinal process, the qualities that underpin elite sports performance, and how coaches can facilitate the developmental process of talented athletes. Despite the continued interest given to issues of talent identification and development, recent literature highlights the low predictive value of applied and theoretical talent identification models. Talent is the expression of a complex and multidimensional phenomenon, where, despite the existing practical recommendations, many coaches and stakeholders continue to fail to adequately value the distinction between growth, maturation, and training age. Technological resources have enabled important advances, however, this has been limited essentially to defining or validating motor skills variables or genetic markers that characterize the most talented athletes. Emerging technological resources and recent methodological advances are enabling integrated assessment and monitoring to include maturational, physiological, biomechanical, and perceptual skills while also creating optimal environments for performance and dealing with injury prevention and recovery

    Time Series Modelling

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    The analysis and modeling of time series is of the utmost importance in various fields of application. This Special Issue is a collection of articles on a wide range of topics, covering stochastic models for time series as well as methods for their analysis, univariate and multivariate time series, real-valued and discrete-valued time series, applications of time series methods to forecasting and statistical process control, and software implementations of methods and models for time series. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples

    Proceedings of the 19th Sound and Music Computing Conference

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    Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f

    Topological Aspects of Linear Dynamic Networks: Identifiability and Identification

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