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  1. Article ; Online: Elastofibroma Dorsi: Points for Discussion and Future Direction.

    Migliore, Marcello

    World journal of surgery

    2023  Volume 47, Issue 10, Page(s) 2594–2595

    MeSH term(s) Humans ; Soft Tissue Neoplasms/diagnosis ; Soft Tissue Neoplasms/surgery
    Language English
    Publishing date 2023-08-14
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 224043-9
    ISSN 1432-2323 ; 0364-2313
    ISSN (online) 1432-2323
    ISSN 0364-2313
    DOI 10.1007/s00268-023-07134-x
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1336: Show issues Location:
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  2. Article: Primum non nocere: do we really need non-intubated thoracic surgery and robotic assisted thoracic surgery for tracheal airway resection and reconstruction?

    Migliore, Marcello

    Annals of translational medicine

    2022  Volume 9, Issue 24, Page(s) 1750

    Language English
    Publishing date 2022-01-19
    Publishing country China
    Document type Editorial ; Comment
    ZDB-ID 2893931-1
    ISSN 2305-5847 ; 2305-5839
    ISSN (online) 2305-5847
    ISSN 2305-5839
    DOI 10.21037/atm-21-5423
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Mathematical generation of data-driven hippocampal CA1 pyramidal neurons and interneurons copies via A-GLIF models for large-scale networks covering the experimental variability range.

    Marasco, A / Tribuzi, C / Iuorio, A / Migliore, M

    Mathematical biosciences

    2024  Volume 371, Page(s) 109179

    Abstract: Efficient and accurate large-scale networks are a fundamental tool in modeling brain areas, to advance our understanding of neuronal dynamics. However, their implementation faces two key issues: computational efficiency and heterogeneity. Computational ... ...

    Abstract Efficient and accurate large-scale networks are a fundamental tool in modeling brain areas, to advance our understanding of neuronal dynamics. However, their implementation faces two key issues: computational efficiency and heterogeneity. Computational efficiency is achieved using simplified neurons, whereas there are no practical solutions available to solve the problem of reproducing in a large-scale network the experimentally observed heterogeneity of the intrinsic properties of neurons. This is important, because the use of identical nodes in a network can generate artifacts which can hinder an adequate representation of the properties of a real network. To this aim, we introduce a mathematical procedure to generate an arbitrary large number of copies of simplified hippocampal CA1 pyramidal neurons and interneurons models, which exhibit the full range of firing dynamics observed in these cells - including adapting, non-adapting and bursting. For this purpose, we rely on a recently published adaptive generalized leaky integrate-and-fire (A-GLIF) modeling approach, leveraging on its ability to reproduce the rich set of electrophysiological behaviors of these types of neurons under a variety of different stimulation currents. The generation procedure is based on a perturbation of model's parameters related to the initial data, firing block, and internal dynamics, and suitably validated against experimental data to ensure that the firing dynamics of any given cell copy remains within the experimental range. A classification procedure confirmed that the firing behavior of most of the pyramidal/interneuron copies was consistent with the experimental data. This approach allows to obtain heterogeneous copies with mathematically controlled firing properties. A full set of heterogeneous neurons composing the CA1 region of a rat hippocampus (approximately 1.2 million neurons), are provided in a database freely available in the live paper section of the EBRAINS platform. By adapting the underlying A-GLIF framework, it will be possible to extend the numerical approach presented here to create, in a mathematically controlled manner, an arbitrarily large number of non-identical copies of cell populations with firing properties related to other brain areas.
    MeSH term(s) Interneurons/physiology ; Pyramidal Cells/physiology ; CA1 Region, Hippocampal/physiology ; CA1 Region, Hippocampal/cytology ; Models, Neurological ; Animals ; Rats ; Action Potentials/physiology ; Nerve Net/physiology ; Computer Simulation
    Language English
    Publishing date 2024-03-21
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1126-5
    ISSN 1879-3134 ; 0025-5564
    ISSN (online) 1879-3134
    ISSN 0025-5564
    DOI 10.1016/j.mbs.2024.109179
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    Zs.A 1534: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  4. Article: Ground glass opacities of the lung before, during and post COVID-19 pandemic.

    Migliore, Marcello

    Annals of translational medicine

    2021  Volume 9, Issue 13, Page(s) 1042

    Language English
    Publishing date 2021-08-22
    Publishing country China
    Document type Editorial
    ZDB-ID 2893931-1
    ISSN 2305-5847 ; 2305-5839
    ISSN (online) 2305-5847
    ISSN 2305-5839
    DOI 10.21037/atm-21-2095
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  5. Article: Present and future of hyperthermic intrathoracic chemotherapy (HITHOC) in thoracic surgical oncology.

    Migliore, Marcello

    Annals of translational medicine

    2021  Volume 9, Issue 11, Page(s) 952

    Language English
    Publishing date 2021-08-04
    Publishing country China
    Document type Editorial
    ZDB-ID 2893931-1
    ISSN 2305-5847 ; 2305-5839
    ISSN (online) 2305-5847
    ISSN 2305-5839
    DOI 10.21037/atm-21-1040
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Which is the best approach for minimally invasive oesophagectomy?

    Migliore, Marcello

    European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery

    2021  Volume 59, Issue 6, Page(s) 1285–1286

    MeSH term(s) Esophageal Neoplasms/surgery ; Esophagectomy ; Humans ; Minimally Invasive Surgical Procedures
    Language English
    Publishing date 2021-02-05
    Publishing country Germany
    Document type Editorial ; Comment
    ZDB-ID 639293-3
    ISSN 1873-734X ; 1010-7940 ; 1567-4258
    ISSN (online) 1873-734X
    ISSN 1010-7940 ; 1567-4258
    DOI 10.1093/ejcts/ezab041
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2303: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  7. Article ; Online: Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models.

    Marasco, A / Tribuzi, C / Lupascu, C A / Migliore, M

    Mathematical biosciences

    2024  , Page(s) 109192

    Abstract: Computational models of brain regions are crucial for understanding neuronal network dynamics and the emergence of cognitive functions. However, current supercomputing limitations hinder the implementation of large networks with millions of morphological ...

    Abstract Computational models of brain regions are crucial for understanding neuronal network dynamics and the emergence of cognitive functions. However, current supercomputing limitations hinder the implementation of large networks with millions of morphological and biophysical accurate neurons. Consequently, research has focused on simplified spiking neuron models, ranging from the computationally fast Leaky Integrate and Fire (LIF) linear models to more sophisticated non-linear implementations like Adaptive Exponential (AdEX) and Izhikevic models, through Generalized Leaky Integrate and Fire (GLIF) approaches. However, in almost all cases, these models are tuned (and can be validated) only under constant current injections and they may not, in general, also reproduce experimental findings under variable currents. This study introduces an Adaptive GLIF (A-GLIF) approach that addresses this limitation by incorporating a new set of update rules. The extended A-GLIF model successfully reproduces both constant and variable current inputs, and it was validated against the results obtained using a biophysical accurate model neuron. This enhancement provides researchers with a tool to optimize spiking neuron models using classic experimental traces under constant current injections, reliably predicting responses to synaptic inputs, which can be confidently used for large-scale network implementations.
    Language English
    Publishing date 2024-04-17
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1126-5
    ISSN 1879-3134 ; 0025-5564
    ISSN (online) 1879-3134
    ISSN 0025-5564
    DOI 10.1016/j.mbs.2024.109192
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1534: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  8. Article: Malignant pleural mesothelioma: between pragmatism and hope.

    Migliore, Marcello

    Annals of translational medicine

    2020  Volume 8, Issue 14, Page(s) 896

    Language English
    Publishing date 2020-07-22
    Publishing country China
    Document type Editorial
    ZDB-ID 2893931-1
    ISSN 2305-5847 ; 2305-5839
    ISSN (online) 2305-5847
    ISSN 2305-5839
    DOI 10.21037/atm.2020.03.58
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  9. Article ; Online: A realistic computational model for the formation of a Place Cell.

    Mazzara, Camille / Migliore, Michele

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 21763

    Abstract: Hippocampal Place Cells (PCs) are pyramidal neurons showing spatially localized firing when an animal gets into a specific area within an environment. Because of their obvious and clear relation with specific cognitive functions, Place Cells operations ... ...

    Abstract Hippocampal Place Cells (PCs) are pyramidal neurons showing spatially localized firing when an animal gets into a specific area within an environment. Because of their obvious and clear relation with specific cognitive functions, Place Cells operations and modulations are intensely studied experimentally. However, although a lot of data have been gathered since their discovery, the cellular processes that interplay to turn a hippocampal pyramidal neuron into a Place Cell are still not completely understood. Here, we used a morphologically and biophysically detailed computational model of a CA1 pyramidal neuron to show how, and under which conditions, it can turn into a neuron coding for a specific cue location, through the self-organization of its synaptic inputs in response to external signals targeting different dendritic layers. Our results show that the model is consistent with experimental findings demonstrating PCs stability within the same spatial context over different trajectories, environment rotations, and place field remapping to adapt to changes in the environment. To date, this is the only biophysically and morphologically accurate cellular model of PCs formation, which can be directly used in physiologically accurate microcircuits and large-scale model networks to study cognitive functions and dysfunctions at cellular level.
    MeSH term(s) Animals ; Place Cells ; Neurons/physiology ; Pyramidal Cells/physiology ; Hippocampus/physiology ; Synapses/physiology ; CA1 Region, Hippocampal/physiology ; Action Potentials/physiology
    Language English
    Publishing date 2023-12-08
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-48183-5
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  10. Article ; Online: An explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry.

    Coppolino, Simone / Migliore, Michele

    Neural networks : the official journal of the International Neural Network Society

    2023  Volume 163, Page(s) 97–107

    Abstract: Learning to navigate a complex environment is not a difficult task for a mammal. For example, finding the correct way to exit a maze following a sequence of cues, does not need a long training session. Just a single or a few runs through a new ... ...

    Abstract Learning to navigate a complex environment is not a difficult task for a mammal. For example, finding the correct way to exit a maze following a sequence of cues, does not need a long training session. Just a single or a few runs through a new environment is, in most cases, sufficient to learn an exit path starting from anywhere in the maze. This ability is in striking contrast with the well-known difficulty that any deep learning algorithm has in learning a trajectory through a sequence of objects. Being able to learn an arbitrarily long sequence of objects to reach a specific place could take, in general, prohibitively long training sessions. This is a clear indication that current artificial intelligence methods are essentially unable to capture the way in which a real brain implements a cognitive function. In previous work, we have proposed a proof-of-principle model demonstrating how, using hippocampal circuitry, it is possible to learn an arbitrary sequence of known objects in a single trial. We called this model SLT (Single Learning Trial). In the current work, we extend this model, which we will call e-STL, to introduce the capability of navigating a classic four-arms maze to learn, in a single trial, the correct path to reach an exit ignoring dead ends. We show the conditions under which the e-SLT network, including cells coding for places, head-direction, and objects, can robustly and efficiently implement a fundamental cognitive function. The results shed light on the possible circuit organization and operation of the hippocampus and may represent the building block of a new generation of artificial intelligence algorithms for spatial navigation.
    MeSH term(s) Animals ; Artificial Intelligence ; Spatial Navigation ; Hippocampus ; Cues ; Brain ; Maze Learning ; Mammals
    Language English
    Publishing date 2023-03-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 740542-x
    ISSN 1879-2782 ; 0893-6080
    ISSN (online) 1879-2782
    ISSN 0893-6080
    DOI 10.1016/j.neunet.2023.03.030
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