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Mograbi DC, Hall S, Arantes B, Huntley J. The cognitive neuroscience of self-awareness: Current framework, clinical implications, and future research directions. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2024; 15:e1670. [PMID: 38043919 DOI: 10.1002/wcs.1670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/04/2023] [Accepted: 11/06/2023] [Indexed: 12/05/2023]
Abstract
Self-awareness, the ability to take oneself as the object of awareness, has been an enigma for our species, with different answers to this question being provided by religion, philosophy, and, more recently, science. The current review aims to discuss the neurocognitive mechanisms underlying self-awareness. The multidimensional nature of self-awareness will be explored, suggesting how it can be thought of as an emergent property observed in different cognitive complexity levels, within a predictive coding approach. A presentation of alterations of self-awareness in neuropsychiatric conditions will ground a discussion on alternative frameworks to understand this phenomenon, in health and psychopathology, with future research directions being indicated to fill current gaps in the literature. This article is categorized under: Philosophy > Consciousness Psychology > Brain Function and Dysfunction Neuroscience > Cognition.
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Affiliation(s)
- Daniel C Mograbi
- Department of Psychology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Simon Hall
- Camden and Islington NHS Foundation Trust, London, UK
| | - Beatriz Arantes
- Department of Psychology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Jonathan Huntley
- Division of Psychiatry, University College London, London, UK
- Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
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Kéfi S, Génin A, Garcia-Mayor A, Guirado E, Cabral JS, Berdugo M, Guerber J, Solé R, Maestre FT. Self-organization as a mechanism of resilience in dryland ecosystems. Proc Natl Acad Sci U S A 2024; 121:e2305153121. [PMID: 38300860 PMCID: PMC10861902 DOI: 10.1073/pnas.2305153121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 12/11/2023] [Indexed: 02/03/2024] Open
Abstract
Self-organized spatial patterns are a common feature of complex systems, ranging from microbial communities to mussel beds and drylands. While the theoretical implications of these patterns for ecosystem-level processes, such as functioning and resilience, have been extensively studied, empirical evidence remains scarce. To address this gap, we analyzed global drylands along an aridity gradient using remote sensing, field data, and modeling. We found that the spatial structure of the vegetation strengthens as aridity increases, which is associated with the maintenance of a high level of soil multifunctionality, even as aridity levels rise up to a certain threshold. The combination of these results with those of two individual-based models indicate that self-organized vegetation patterns not only form in response to stressful environmental conditions but also provide drylands with the ability to adapt to changing conditions while maintaining their functioning, an adaptive capacity which is lost in degraded ecosystems. Self-organization thereby plays a vital role in enhancing the resilience of drylands. Overall, our findings contribute to a deeper understanding of the relationship between spatial vegetation patterns and dryland resilience. They also represent a significant step forward in the development of indicators for ecosystem resilience, which are critical tools for managing and preserving these valuable ecosystems in a warmer and more arid world.
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Affiliation(s)
- Sonia Kéfi
- Institut des Sciences de l'Evolution de Montpellier (ISEM), CNRS, Univ. de Montpellier, Institut de recherche pour le développement (IRD), Montpellier 34095, France
- Santa Fe Institute, Santa Fe, NM 87501
- Ecosystem Modeling Group, Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
| | - Alexandre Génin
- Institut des Sciences de l'Evolution de Montpellier (ISEM), CNRS, Univ. de Montpellier, Institut de recherche pour le développement (IRD), Montpellier 34095, France
- Environmental Sciences, Copernicus Institute of Sustainable Development, Utrecht University, Utrecht 3508TC, The Netherlands
- Estación Costera de Investigaciones Marinas, Pontificia Universidad Católica de Chile, Las Cruces 2690000, Chile
| | - Angeles Garcia-Mayor
- Environmental Sciences, Copernicus Institute of Sustainable Development, Utrecht University, Utrecht 3508TC, The Netherlands
- Department of Biodiversity, Ecology and Evolution, Faculty of Biology, Complutense University of Madrid, Madrid 28040, Spain
| | - Emilio Guirado
- Instituto Multidisciplinar para el Estudio del Medio "Ramón Margalef," Universidad de Alicante, Alicante 03690, Spain
| | - Juliano S Cabral
- Ecosystem Modeling Group, Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
- School of Biosciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Miguel Berdugo
- Department of Biodiversity, Ecology and Evolution, Faculty of Biology, Complutense University of Madrid, Madrid 28040, Spain
| | - Josquin Guerber
- Institut des Sciences de l'Evolution de Montpellier (ISEM), CNRS, Univ. de Montpellier, Institut de recherche pour le développement (IRD), Montpellier 34095, France
- Centre d'Ecologie et des Sciences de la Conservation (CESCO), MNHN, CNRS, Sorbonne Univ., 75005 Paris, France
| | - Ricard Solé
- Santa Fe Institute, Santa Fe, NM 87501
- Catalan Institution for Research and Advanced Studies-Complex Systems Lab, Universitat Pompeu Fabra, Barcelona 08003, Spain
- Institute of Evolutionary Biology, Spanish National Research Council (CSIC)-Universitat Pompeu Fabra, Barcelona 08003, Spain
| | - Fernando T Maestre
- Instituto Multidisciplinar para el Estudio del Medio "Ramón Margalef," Universidad de Alicante, Alicante 03690, Spain
- Departamento de Ecología, Universidad de Alicante, Alicante 03690, Spain
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Yuan B, Zhang J, Lyu A, Wu J, Wang Z, Yang M, Liu K, Mou M, Cui P. Emergence and Causality in Complex Systems: A Survey of Causal Emergence and Related Quantitative Studies. ENTROPY (BASEL, SWITZERLAND) 2024; 26:108. [PMID: 38392363 PMCID: PMC10887681 DOI: 10.3390/e26020108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 02/24/2024]
Abstract
Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties. On the other hand, causality can exhibit emergence, meaning that new causal laws may arise as we increase the level of abstraction. Causal emergence (CE) theory aims to bridge these two concepts and even employs measures of causality to quantify emergence. This paper provides a comprehensive review of recent advancements in quantitative theories and applications of CE. It focuses on two primary challenges: quantifying CE and identifying it from data. The latter task requires the integration of machine learning and neural network techniques, establishing a significant link between causal emergence and machine learning. We highlight two problem categories: CE with machine learning and CE for machine learning, both of which emphasize the crucial role of effective information (EI) as a measure of causal emergence. The final section of this review explores potential applications and provides insights into future perspectives.
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Affiliation(s)
- Bing Yuan
- Swarma Research, Beijing 100085, China
| | - Jiang Zhang
- Swarma Research, Beijing 100085, China
- School of Systems Sciences, Beijing Normal University, Beijing 100875, China
| | - Aobo Lyu
- Department of Electrical and Systems Engineering, Washington University, St. Louis, MO 63130, USA
| | - Jiayun Wu
- Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
| | - Zhipeng Wang
- School of Systems Sciences, Beijing Normal University, Beijing 100875, China
| | - Mingzhe Yang
- School of Systems Sciences, Beijing Normal University, Beijing 100875, China
| | - Kaiwei Liu
- School of Systems Sciences, Beijing Normal University, Beijing 100875, China
| | - Muyun Mou
- School of Systems Sciences, Beijing Normal University, Beijing 100875, China
| | - Peng Cui
- Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
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Baumeister A. The historical and philosophical roots of emergentism in the neurosciences. JOURNAL OF THE HISTORY OF THE NEUROSCIENCES 2024; 33:73-88. [PMID: 37682692 DOI: 10.1080/0964704x.2023.2248193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
Understanding and characterizing the relationship between mental phenomena and the brain is a huge challenge for modern neuroscience. No doubt, the conservative orthodox view of this relationship can be described as physicalist. Physicalism is the idea that, no matter how enigmatic mental phenomena may seem, they are nevertheless completely describable in physical and material terms. Still, despite centuries of effort, aspects of mind, such as the qualitative nature of subjective experience, have defied physical characterization. In the early 1920s, emergentism was advanced to explain the relationship between physical reality and higher-order phenomena, including life and mind. According to emergentism, such higher-order phenomena are derivative of and, at the same time, autonomous to underlying physical reality. This article describes the historical and philosophical development of emergentist theses, particularly as they have been treated in the neurosciences.
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Affiliation(s)
- Alan Baumeister
- Department of Psychology, Louisiana State University College of Humanities and Social Sciences, Baton Rouge, Louisiana, USA
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Gómez-Márquez J. Reflections upon a new definition of life. THE SCIENCE OF NATURE - NATURWISSENSCHAFTEN 2023; 110:53. [PMID: 37917201 DOI: 10.1007/s00114-023-01882-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023]
Abstract
What is life? Multiple definitions have been proposed to answer this question, but unfortunately, none of them has reached the consensus of the scientific community. Here, the strategy used to define what life is was based on first establishing which characteristics are common to all living systems (organic nature, entropy-producing system, self-organizing, reworkable pre-program, capacity to interact and adapt, reproduction and evolution) and from them constructing the definition taking into account that reproduction and evolution are not essential for life. On this basis, life is defined as an interactive process occurring in entropy-producing, adaptive, and informative (organic) systems. An unforeseen consequence of the inseparable duality between the system (living being) and the process (life) is the interchangeability of the elements of the definition to obtain other equally valid alternatives. In addition, in the light of this definition, cases of temporarily lifeless living systems (viruses, dormant seeds, and ultracold cells) are analyzed, as well as the status of artificial life entities and the hypothetical nature of extraterrestrial life. All living systems are perishable because the passage of time leads to increasing entropy. Life must create order by continuously producing disorder and exporting it to the environment and so we move and stay in the phase transition between order and chaos, far from equilibrium, thanks to the input of energy from the outside. However, the passage of time eventually leads us to an end in which life disappears and entropy increases.
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Affiliation(s)
- Jaime Gómez-Márquez
- Department of Biochemistry and Molecular Biology, Bldg. CIBUS-Faculty of Biology, University of Santiago de Compostela, 15782, Santiago de Compostela, Galicia, Spain.
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Marku M, Pancaldi V. From time-series transcriptomics to gene regulatory networks: A review on inference methods. PLoS Comput Biol 2023; 19:e1011254. [PMID: 37561790 PMCID: PMC10414591 DOI: 10.1371/journal.pcbi.1011254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023] Open
Abstract
Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.
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Affiliation(s)
- Malvina Marku
- CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
| | - Vera Pancaldi
- CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
- Barcelona Supercomputing Center, Barcelona, Spain
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Merbis W, de Mulatier C, Corboz P. Efficient simulations of epidemic models with tensor networks: Application to the one-dimensional susceptible-infected-susceptible model. Phys Rev E 2023; 108:024303. [PMID: 37723790 DOI: 10.1103/physreve.108.024303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 07/20/2023] [Indexed: 09/20/2023]
Abstract
The contact process is an emblematic model of a nonequilibrium system, containing a phase transition between inactive and active dynamical regimes. In the epidemiological context, the model is known as the susceptible-infected-susceptible model, and it is widely used to describe contagious spreading. In this work, we demonstrate how accurate and efficient representations of the full probability distribution over all configurations of the contact process on a one-dimensional chain can be obtained by means of matrix product states (MPSs). We modify and adapt MPS methods from many-body quantum systems to study the classical distributions of the driven contact process at late times. We give accurate and efficient results for the distribution of large gaps, and illustrate the advantage of our methods over Monte Carlo simulations. Furthermore, we study the large deviation statistics of the dynamical activity, defined as the total number of configuration changes along a trajectory, and investigate quantum-inspired entropic measures, based on the second Rényi entropy.
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Affiliation(s)
- Wout Merbis
- Dutch Institute for Emergent Phenomena (DIEP) & Institute for Theoretical Physics (ITFA), University of Amsterdam, 1090 GL Amsterdam, The Netherlands
| | - Clélia de Mulatier
- Dutch Institute for Emergent Phenomena (DIEP) & Institute for Theoretical Physics (ITFA), University of Amsterdam, 1090 GL Amsterdam, The Netherlands
| | - Philippe Corboz
- Dutch Institute for Emergent Phenomena (DIEP) & Institute for Theoretical Physics (ITFA), University of Amsterdam, 1090 GL Amsterdam, The Netherlands
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Merbis W, de Domenico M. Emergent information dynamics in many-body interconnected systems. Phys Rev E 2023; 108:014312. [PMID: 37583168 DOI: 10.1103/physreve.108.014312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 07/10/2023] [Indexed: 08/17/2023]
Abstract
The information implicitly represented in the state of physical systems allows for their analysis using analytical techniques from statistical mechanics and information theory. This approach has been successfully applied to complex networks, including biophysical systems such as virus-host protein-protein interactions and whole-brain models in health and disease, drawing inspiration from quantum statistical physics. Here we propose a general mathematical framework for modeling information dynamics on complex networks, where the internal node states are vector valued, allowing each node to carry multiple types of information. This setup is relevant for various biophysical and sociotechnological models of complex systems, ranging from viral dynamics on networks to models of opinion dynamics and social contagion. Instead of focusing on node-node interactions, we shift our attention to the flow of information between network configurations. We uncover fundamental differences between widely used spin models on networks, such as voter and kinetic dynamics, which cannot be detected through classical node-based analysis. We illustrate the mathematical framework further through an exemplary application to epidemic spreading on a low-dimensional network. Our model provides an opportunity to adapt powerful analytical methods from quantum many-body systems to study the interplay between structure and dynamics in interconnected systems.
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Affiliation(s)
- Wout Merbis
- Dutch Institute for Emergent Phenomena (DIEP), Institute for Theoretical Physics (ITFA), University of Amsterdam, 1090 GL Amsterdam, The Netherlands
| | - Manlio de Domenico
- Department of Physics and Astronomy "Galileo Galilei," University of Padua, Via F. Marzolo 8, 315126 Padua, Italy and Istituto Nazionale di Fisica Nucleare, Sez. Padua, Italy
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Recanatini M, Menestrina L. Network modeling helps to tackle the complexity of drug-disease systems. WIREs Mech Dis 2023:e1607. [PMID: 36958762 DOI: 10.1002/wsbm.1607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/03/2023] [Accepted: 03/03/2023] [Indexed: 03/25/2023]
Abstract
From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug-disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of "disease module", as well as the introduction of more articulated models, like multi-scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug-disease systems highlighting some open issues. This article is categorized under: Neurological Diseases > Computational Models Infectious Diseases > Computational Models Cardiovascular Diseases > Computational Models.
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Affiliation(s)
- Maurizio Recanatini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, Bologna, 40126, Italy
| | - Luca Menestrina
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, Bologna, 40126, Italy
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Varley TF. Flickering Emergences: The Question of Locality in Information-Theoretic Approaches to Emergence. ENTROPY (BASEL, SWITZERLAND) 2022; 25:e25010054. [PMID: 36673195 PMCID: PMC9858457 DOI: 10.3390/e25010054] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 12/08/2022] [Accepted: 12/25/2022] [Indexed: 05/25/2023]
Abstract
"Emergence", the phenomenon where a complex system displays properties, behaviours, or dynamics not trivially reducible to its constituent elements, is one of the defining properties of complex systems. Recently, there has been a concerted effort to formally define emergence using the mathematical framework of information theory, which proposes that emergence can be understood in terms of how the states of wholes and parts collectively disclose information about the system's collective future. In this paper, we show how a common, foundational component of information-theoretic approaches to emergence implies an inherent instability to emergent properties, which we call flickering emergence. A system may, on average, display a meaningful emergent property (be it an informative coarse-graining, or higher-order synergy), but for particular configurations, that emergent property falls apart and becomes misinformative. We show existence proofs that flickering emergence occurs in two different frameworks (one based on coarse-graining and another based on multivariate information decomposition) and argue that any approach based on temporal mutual information will display it. Finally, we argue that flickering emergence should not be a disqualifying property of any model of emergence, but that it should be accounted for when attempting to theorize about how emergence relates to practical models of the natural world.
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Affiliation(s)
- Thomas F. Varley
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN 47405, USA;
- School of Informatics, Computing, & Engineering, Indiana University Bloomington, Bloomington, IN 47405, USA
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