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  1. Article ; Online: Brain network analysis: a practical tutorial.

    Bassett, Danielle S

    Brain : a journal of neurology

    2019  Volume 139, Issue 11, Page(s) 3048–3049

    Language English
    Publishing date 2019-08-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 80072-7
    ISSN 1460-2156 ; 0006-8950
    ISSN (online) 1460-2156
    ISSN 0006-8950
    DOI 10.1093/brain/aww232
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Quantifying the compressibility of complex networks.

    Lynn, Christopher W / Bassett, Danielle S

    Proceedings of the National Academy of Sciences of the United States of America

    2021  Volume 118, Issue 32

    Abstract: Many complex networks depend upon biological entities for their preservation. Such entities, from human cognition to evolution, must first encode and then replicate those networks under marked resource constraints. Networks that survive are those that ... ...

    Abstract Many complex networks depend upon biological entities for their preservation. Such entities, from human cognition to evolution, must first encode and then replicate those networks under marked resource constraints. Networks that survive are those that are amenable to constrained encoding-or, in other words, are compressible. But how compressible is a network? And what features make one network more compressible than another? Here, we answer these questions by modeling networks as information sources before compressing them using rate-distortion theory. Each network yields a unique rate-distortion curve, which specifies the minimal amount of information that remains at a given scale of description. A natural definition then emerges for the compressibility of a network: the amount of information that can be removed via compression, averaged across all scales. Analyzing an array of real and model networks, we demonstrate that compressibility increases with two common network properties: transitivity (or clustering) and degree heterogeneity. These results indicate that hierarchical organization-which is characterized by modular structure and heterogeneous degrees-facilitates compression in complex networks. Generally, our framework sheds light on the interplay between a network's structure and its capacity to be compressed, enabling investigations into the role of compression in shaping real-world networks.
    MeSH term(s) Algorithms ; Cluster Analysis ; Community Networks ; Computer Communication Networks ; Data Compression ; Humans ; Models, Theoretical ; Random Allocation
    Language English
    Publishing date 2021-08-05
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2023473118
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: How humans learn and represent networks.

    Lynn, Christopher W / Bassett, Danielle S

    Proceedings of the National Academy of Sciences of the United States of America

    2020  Volume 117, Issue 47, Page(s) 29407–29415

    MeSH term(s) Behavior/physiology ; Cognition/physiology ; Humans ; Learning/physiology ; Markov Chains ; Models, Psychological
    Language English
    Publishing date 2020-11-23
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.1912328117
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Brain network analysis: a practical tutorial.

    Bassett, Danielle S

    Brain : a journal of neurology

    2016  Volume 139, Issue 11, Page(s) 3048–3049

    Language English
    Publishing date 2016-11-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 80072-7
    ISSN 1460-2156 ; 0006-8950
    ISSN (online) 1460-2156
    ISSN 0006-8950
    DOI 10.1093/brain/aww232
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Ui II Zs.26: Show issues Location:
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  5. Article ; Online: A multilayer network model of neuron-astrocyte populations in vitro reveals mGluR

    Schroeder, Margaret E / Bassett, Danielle S / Meaney, David F

    Network neuroscience (Cambridge, Mass.)

    2022  Volume 6, Issue 2, Page(s) 499–527

    Abstract: Astrocytes communicate bidirectionally with neurons, enhancing synaptic plasticity and promoting the synchronization of neuronal microcircuits. Despite recent advances in understanding neuron-astrocyte signaling, little is known about astrocytic ... ...

    Abstract Astrocytes communicate bidirectionally with neurons, enhancing synaptic plasticity and promoting the synchronization of neuronal microcircuits. Despite recent advances in understanding neuron-astrocyte signaling, little is known about astrocytic modulation of neuronal activity at the population level, particularly in disease or following injury. We used high-speed calcium imaging of mixed cortical cultures in vitro to determine how population activity changes after disruption of glutamatergic signaling and mechanical injury. We constructed a multilayer network model of neuron-astrocyte connectivity, which captured distinct topology and response behavior from single-cell-type networks. mGluR
    Language English
    Publishing date 2022-06-01
    Publishing country United States
    Document type Journal Article
    ISSN 2472-1751
    ISSN (online) 2472-1751
    DOI 10.1162/netn_a_00227
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Dynamic representations in networked neural systems.

    Ju, Harang / Bassett, Danielle S

    Nature neuroscience

    2020  Volume 23, Issue 8, Page(s) 908–917

    Abstract: A group of neurons can generate patterns of activity that represent information about stimuli; subsequently, the group can transform and transmit activity patterns across synapses to spatially distributed areas. Recent studies in neuroscience have begun ... ...

    Abstract A group of neurons can generate patterns of activity that represent information about stimuli; subsequently, the group can transform and transmit activity patterns across synapses to spatially distributed areas. Recent studies in neuroscience have begun to independently address the two components of information processing: the representation of stimuli in neural activity and the transmission of information in networks that model neural interactions. Yet only recently are studies seeking to link these two types of approaches. Here we briefly review the two separate bodies of literature; we then review the recent strides made to address this gap. We continue with a discussion of how patterns of activity evolve from one representation to another, forming dynamic representations that unfold on the underlying network. Our goal is to offer a holistic framework for understanding and describing neural information representation and transmission while revealing exciting frontiers for future research.
    MeSH term(s) Animals ; Brain/physiology ; Cognition/physiology ; Humans ; Nerve Net/physiology ; Neural Networks, Computer ; Neurons/physiology ; Synaptic Transmission/physiology
    Language English
    Publishing date 2020-06-15
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Review
    ZDB-ID 1420596-8
    ISSN 1546-1726 ; 1097-6256
    ISSN (online) 1546-1726
    ISSN 1097-6256
    DOI 10.1038/s41593-020-0653-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 4891: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 67: Show issues

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  7. Article ; Online: On the Nature of Explanations Offered by Network Science: A Perspective From and for Practicing Neuroscientists.

    Bertolero, Maxwell A / Bassett, Danielle S

    Topics in cognitive science

    2020  Volume 12, Issue 4, Page(s) 1272–1293

    Abstract: Network neuroscience represents the brain as a collection of regions and inter-regional connections. Given its ability to formalize systems-level models, network neuroscience has generated unique explanations of neural function and behavior. The ... ...

    Abstract Network neuroscience represents the brain as a collection of regions and inter-regional connections. Given its ability to formalize systems-level models, network neuroscience has generated unique explanations of neural function and behavior. The mechanistic status of these explanations and how they can contribute to and fit within the field of neuroscience as a whole has received careful treatment from philosophers. However, these philosophical contributions have not yet reached many neuroscientists. Here we complement formal philosophical efforts by providing an applied perspective from and for neuroscientists. We discuss the mechanistic status of the explanations offered by network neuroscience and how they contribute to, enhance, and interdigitate with other types of explanations in neuroscience. In doing so, we rely on philosophical work concerning the role of causality, scale, and mechanisms in scientific explanations. In particular, we make the distinction between an explanation and the evidence supporting that explanation, and we argue for a scale-free nature of mechanistic explanations. In the course of these discussions, we hope to provide a useful applied framework in which network neuroscience explanations can be exercised across scales and combined with other fields of neuroscience to gain deeper insights into the brain and behavior.
    MeSH term(s) Brain/physiology ; Causality ; Humans ; Neurosciences
    Language English
    Publishing date 2020-05-22
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2482883-X
    ISSN 1756-8765 ; 1756-8757
    ISSN (online) 1756-8765
    ISSN 1756-8757
    DOI 10.1111/tops.12504
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: KONNEKTOMIK. DAS NETZWERK DES GEISTES. Unsere mentale Aktivität entsteht aus dem sorgfältig abgestimmten Zusammenspiel verschiedener Hirnareale.

    Bertolero, Max / Bassett, Danielle S.

    Spektrum der Wissenschaft : Spezial

    2021  Volume 4, Issue Das Gehirn, Page(s) 28

    Language German
    Document type Article
    ZDB-ID 2938618-4
    ISSN 2625-7947 ; 2193-4452
    Database Current Contents Medicine

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    Zs.A 2122 -Spezial-: Show issues Location:
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  9. Article ; Online: Invertible generalized synchronization: A putative mechanism for implicit learning in neural systems.

    Lu, Zhixin / Bassett, Danielle S

    Chaos (Woodbury, N.Y.)

    2020  Volume 30, Issue 6, Page(s) 63133

    Abstract: Regardless of the marked differences between biological and artificial neural systems, one fundamental similarity is that they are essentially dynamical systems that can learn to imitate other dynamical systems whose governing equations are unknown. The ... ...

    Abstract Regardless of the marked differences between biological and artificial neural systems, one fundamental similarity is that they are essentially dynamical systems that can learn to imitate other dynamical systems whose governing equations are unknown. The brain is able to learn the dynamic nature of the physical world via experience; analogously, artificial neural systems such as reservoir computing networks (RCNs) can learn the long-term behavior of complex dynamical systems from data. Recent work has shown that the mechanism of such learning in RCNs is invertible generalized synchronization (IGS). Yet, whether IGS is also the mechanism of learning in biological systems remains unclear. To shed light on this question, we draw inspiration from features of the human brain to propose a general and biologically feasible learning framework that utilizes IGS. To evaluate the framework's relevance, we construct several distinct neural network models as instantiations of the proposed framework. Regardless of their particularities, these neural network models can consistently learn to imitate other dynamical processes with a biologically feasible adaptation rule that modulates the strength of synapses. Further, we observe and theoretically explain the spontaneous emergence of four distinct phenomena reminiscent of cognitive functions: (i) learning multiple dynamics; (ii) switching among the imitations of multiple dynamical systems, either spontaneously or driven by external cues; (iii) filling-in missing variables from incomplete observations; and (iv) deciphering superimposed input from different dynamical systems. Collectively, our findings support the notion that biological neural networks can learn the dynamic nature of their environment through the mechanism of IGS.
    MeSH term(s) Humans ; Learning/physiology ; Models, Neurological ; Nerve Net/physiology ; Neural Networks, Computer ; Synapses
    Language English
    Publishing date 2020-07-02
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1472677-4
    ISSN 1089-7682 ; 1054-1500
    ISSN (online) 1089-7682
    ISSN 1054-1500
    DOI 10.1063/5.0004344
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Network architectures supporting learnability.

    Zurn, Perry / Bassett, Danielle S

    Philosophical transactions of the Royal Society of London. Series B, Biological sciences

    2020  Volume 375, Issue 1796, Page(s) 20190323

    Abstract: Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the ... ...

    Abstract Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the computational unit-the brain-that encodes and processes the information. That is, learning is reliant on integrated network architectures at two levels: the epistemic and the computational, or the conceptual and the neural. Motivated by a wish to understand conventional human knowledge, here, we discuss emerging work assessing network constraints on the learnability of relational knowledge, and theories from statistical physics that instantiate the principles of thermodynamics and information theory to offer an explanatory model for such constraints. We then highlight similarities between those constraints on the learnability of relational networks, at one level, and the physical constraints on the development of interconnected patterns in neural systems, at another level, both leading to hierarchically modular networks. To support our discussion of these similarities, we employ an operational distinction between the modeller (e.g. the human brain), the model (e.g. a single human's knowledge) and the modelled (e.g. the information present in our experiences). We then turn to a philosophical discussion of whether and how we can extend our observations to a claim regarding explanation and mechanism for knowledge acquisition. What relation between hierarchical networks, at the conceptual and neural levels, best facilitate learning? Are the architectures of optimally learnable networks a topological reflection of the architectures of comparably developed neural networks? Finally, we contribute to a unified approach to hierarchies and levels in biological networks by proposing several epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.
    MeSH term(s) Knowledge ; Learning ; Nerve Net ; Physics ; Thermodynamics
    Language English
    Publishing date 2020-02-24
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 208382-6
    ISSN 1471-2970 ; 0080-4622 ; 0264-3839 ; 0962-8436
    ISSN (online) 1471-2970
    ISSN 0080-4622 ; 0264-3839 ; 0962-8436
    DOI 10.1098/rstb.2019.0323
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Einzelsignatur: Show issues

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