Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Since 2003, when spontaneous activity in cortical slices was first found to follow scale-free statistical distributions in size and duration, increasing experimental evidences and theoretical models have been reported in the literature supporting the emergence of evidence of scale invariance in the cortex. Although strongly debated, such results refer to many different in vitro and in vivo preparations (awake monkeys, anesthetized rats and cats, in vitro slices and dissociated cultures), suggesting that power law distributions and scale free correlations are a very general and robust feature of cortical activity that has been conserved across species as specific substrate for information storage, transmission and processing. Equally important is that the features reminiscent of scale invariance and criticality are observed at scale spanning from the level of interacting arrays of neurons all the way up to correlations across the entire brain. Moreover, the existing relationship between features of structural connectivity and functional critical states remains partly unclear, although investigated with both analyses of experimental data and in silico models. Thus, if we accept that the brain operates near a critical point, little is known about the causes and/or consequences of a loss of criticality and its relation with brain diseases (e.g. epilepsy). The study of how pathogenetical mechanisms are related to the critical/non-critical behavior of neuronal networks would likely provide new insights into the cellular and synaptic determinants of the emergence of critical-like dynamics and structures in neural systems. At the same time, the relation between the impaired behavior and the disruption of criticality would help clarify its role in normal brain function. The main objective of this Research Topic is to investigate the emergence/disruption of the emergent critical-like states in healthy/impaired neural systems and to link these phenomena to the underlying cellular and network features, with specific attention to structural connectivity. In particular, we would like this Research Topic to collect contributions coming from the study of neural systems at different levels of architectural complexity (from in vitro neuronal ensembles up to the human brain imaged by fMRI).

Neurosciences. --- Nervous system. --- Neuroscience --- Human Anatomy & Physiology --- Health & Biological Sciences --- Computational models --- in vitro --- in vivo --- network dynamics --- self-organized criticality --- neuronal avalanches --- power law --- Computational models --- in vitro --- in vivo --- network dynamics --- self-organized criticality --- neuronal avalanches --- power law

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Since 2003, when spontaneous activity in cortical slices was first found to follow scale-free statistical distributions in size and duration, increasing experimental evidences and theoretical models have been reported in the literature supporting the emergence of evidence of scale invariance in the cortex. Although strongly debated, such results refer to many different in vitro and in vivo preparations (awake monkeys, anesthetized rats and cats, in vitro slices and dissociated cultures), suggesting that power law distributions and scale free correlations are a very general and robust feature of cortical activity that has been conserved across species as specific substrate for information storage, transmission and processing. Equally important is that the features reminiscent of scale invariance and criticality are observed at scale spanning from the level of interacting arrays of neurons all the way up to correlations across the entire brain. Moreover, the existing relationship between features of structural connectivity and functional critical states remains partly unclear, although investigated with both analyses of experimental data and in silico models. Thus, if we accept that the brain operates near a critical point, little is known about the causes and/or consequences of a loss of criticality and its relation with brain diseases (e.g. epilepsy). The study of how pathogenetical mechanisms are related to the critical/non-critical behavior of neuronal networks would likely provide new insights into the cellular and synaptic determinants of the emergence of critical-like dynamics and structures in neural systems. At the same time, the relation between the impaired behavior and the disruption of criticality would help clarify its role in normal brain function. The main objective of this Research Topic is to investigate the emergence/disruption of the emergent critical-like states in healthy/impaired neural systems and to link these phenomena to the underlying cellular and network features, with specific attention to structural connectivity. In particular, we would like this Research Topic to collect contributions coming from the study of neural systems at different levels of architectural complexity (from in vitro neuronal ensembles up to the human brain imaged by fMRI).

Neurosciences. --- Nervous system. --- Computational models --- in vitro --- in vivo --- network dynamics --- self-organized criticality --- neuronal avalanches --- power law

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Bilingual edition (English/German) / Zweisprachige Ausgabe (deutsch/englisch) From land speculation and exploding rents to climate change and social inequality, we find ourselves in an age of overlapping crises. As such, it is more important than ever that we rethink the ways we live and share, as well as our systems of land and property ownership. Social-Ecological Cooperative Housing presents pioneering cooperative housing projects in Basel, Berlin, Vienna, and Zurich. The initiatives and alternative ownership models showcased demonstrate the potential of cooperative practices for the necessary social-ecological transformation of building and living. This interactive e-book enables and encourages the reader to dive more deeply into individual topics and take action themselves. Zweisprachige Ausgabe (deutsch/englisch) / Bilingual edition (English/German) Von Bodenspekulation und Mietenexplosion bis hin zu Klimawandel und sozialer Ungleichheit: In einer Zeit sich überlagernder Krisen ist es notwendiger denn je, unsere Art zu wohnen und zu teilen sowie das Eigentum an Grund und Boden neu zu denken.Social-Ecological Cooperative Housing stellt wegweisende genossenschaftliche Wohnprojekte in Basel, Berlin, Wien und Zürich vor. Die porträtierten Initiativen und alternativen Eigentumsmodelle zeigen das Potenzial gemeinschaftlicher Praktiken für eine notwendige sozial-ökologische Transformation des Bauens und Wohnens auf. Das interaktive E-Book ermöglicht es den Leser*innen, einzelne Themen je nach Interesse zu vertiefen und regt damit auch zum eigenen Handeln an.

Berlin housing. --- Vienna housing. --- Zurich housing. --- collective ownership. --- commoning. --- commons. --- cooperative housing models. --- cooperative practices. --- housing cooperatives. --- housing projects. --- interactive. --- self-organized. --- sharing culture. --- social-ecological transformation. --- ARCHITECTURE / Individual Architects & Firms / General.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Complexity is a ubiquitous phenomenon in physiology that allows living systems to adapt to external perturbations. Fractal structures, self-organization, nonlinearity, interactions at different scales, and interconnections among systems through anatomical and functional networks, may originate complexity. Biomedical signals from physiological systems may carry information about the system complexity useful to identify physiological states, monitor health, and predict pathological events. Therefore, complexity analysis of biomedical signals is a rapidly evolving field aimed at extracting information on the physiological systems. This book consists of 16 contributions from authors with a strong scientific background in biomedical signals analysis. It includes reviews on the state-of-the-art of complexity studies in specific medical applications, new methods to improve complexity quantifiers, and novel complexity analyses in physiological or clinical scenarios. It presents a wide spectrum of methods investigating the entropic properties, multifractal structure, self-organized criticality, and information dynamics of biomedical signals touching upon three physiological areas: the cardiovascular system, the central nervous system, the heart-brain interactions. The book is aimed at experienced researchers in signal analysis and presents the latest trends in the complexity methods in physiology and medicine with the hope of inspiring future works advancing this fascinating area of research.

Research & information: general --- Mathematics & science --- autonomic nervous function --- heart rate variability (HRV) --- baroreflex sensitivity (BRS) --- photo-plethysmo-graphy (PPG) --- digital volume pulse (DVP) --- percussion entropy index (PEI) --- heart rate variability --- posture --- entropy --- complexity --- cognitive task --- sample entropy --- brain functional networks --- dynamic functional connectivity --- static functional connectivity --- K-means clustering algorithm --- fragmentation --- aging in human population --- factor analysis --- support vector machines classification --- Sampen --- cross-entropy --- autonomic nervous system --- heart rate --- blood pressure --- hypobaric hypoxia --- rehabilitation medicine --- labor --- fetal heart rate --- data compression --- complexity analysis --- nonlinear analysis --- preterm --- Alzheimer’s disease --- brain signals --- single-channel analysis --- biomarker --- refined composite multiscale entropy --- central autonomic network --- interconnectivity --- ECG --- ectopic beat --- baroreflex --- self-organized criticality --- vasovagal syncope --- Zipf’s law --- multifractality --- multiscale complexity --- detrended fluctuation analysis --- self-similarity --- sEMG --- approximate entropy --- fuzzy entropy --- fractal dimension --- recurrence quantification analysis --- correlation dimension --- largest Lyapunov exponent --- time series analysis --- relative consistency --- event-related de/synchronization --- motor imagery --- vector quantization --- information dynamics --- partial information decomposition --- conditional transfer entropy --- network physiology --- multivariate time series analysis --- State–space models --- vector autoregressive model --- penalized regression techniques --- linear prediction --- fNIRS --- brain dynamics --- mental arithmetics --- multiscale --- cardiovascular system --- brain --- information flow --- autonomic nervous function --- heart rate variability (HRV) --- baroreflex sensitivity (BRS) --- photo-plethysmo-graphy (PPG) --- digital volume pulse (DVP) --- percussion entropy index (PEI) --- heart rate variability --- posture --- entropy --- complexity --- cognitive task --- sample entropy --- brain functional networks --- dynamic functional connectivity --- static functional connectivity --- K-means clustering algorithm --- fragmentation --- aging in human population --- factor analysis --- support vector machines classification --- Sampen --- cross-entropy --- autonomic nervous system --- heart rate --- blood pressure --- hypobaric hypoxia --- rehabilitation medicine --- labor --- fetal heart rate --- data compression --- complexity analysis --- nonlinear analysis --- preterm --- Alzheimer’s disease --- brain signals --- single-channel analysis --- biomarker --- refined composite multiscale entropy --- central autonomic network --- interconnectivity --- ECG --- ectopic beat --- baroreflex --- self-organized criticality --- vasovagal syncope --- Zipf’s law --- multifractality --- multiscale complexity --- detrended fluctuation analysis --- self-similarity --- sEMG --- approximate entropy --- fuzzy entropy --- fractal dimension --- recurrence quantification analysis --- correlation dimension --- largest Lyapunov exponent --- time series analysis --- relative consistency --- event-related de/synchronization --- motor imagery --- vector quantization --- information dynamics --- partial information decomposition --- conditional transfer entropy --- network physiology --- multivariate time series analysis --- State–space models --- vector autoregressive model --- penalized regression techniques --- linear prediction --- fNIRS --- brain dynamics --- mental arithmetics --- multiscale --- cardiovascular system --- brain --- information flow