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  1. Article: Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and

    Keller, Daniel / Verasztó, Csaba / Markram, Henry

    Frontiers in neuroanatomy

    2023  Volume 17, Page(s) 1118170

    Abstract: Cells in the mammalian cerebral cortex exhibit layer-dependent patterns in their distribution. Classical methods of determining cell type distributions typically employ a painstaking process of large-scale sampling and characterization of cellular ... ...

    Abstract Cells in the mammalian cerebral cortex exhibit layer-dependent patterns in their distribution. Classical methods of determining cell type distributions typically employ a painstaking process of large-scale sampling and characterization of cellular composition. We found that by combining
    Language English
    Publishing date 2023-03-02
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2452969-2
    ISSN 1662-5129
    ISSN 1662-5129
    DOI 10.3389/fnana.2023.1118170
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  2. Article ; Online: Morphology, physiology and synaptic connectivity of local interneurons in the mouse somatosensory thalamus.

    Simko, Jane / Markram, Henry

    The Journal of physiology

    2021  Volume 599, Issue 22, Page(s) 5085–5101

    Abstract: The thalamic reticular nucleus (TRN) neurons, projecting across the external medullary lamina, have long been considered to be the only significant source of inhibition of the somatosensory ventral posterior (VP) nuclei of the thalamus. Here we report ... ...

    Abstract The thalamic reticular nucleus (TRN) neurons, projecting across the external medullary lamina, have long been considered to be the only significant source of inhibition of the somatosensory ventral posterior (VP) nuclei of the thalamus. Here we report for the first time effective local inhibition and disinhibition in the VP. Inhibitory interneurons were found in GAD67-GFP-expressing mice and studied using in vitro multiple patch clamp. Inhibitory interneurons have expansive bipolar or tripolar morphologies, reach across most of the VP nucleus and display low threshold bursting behaviour. They form triadic and non-triadic synaptic connections onto thalamocortical relay neurons and other interneurons, mediating feedforward inhibition and disinhibition. Synaptic inputs arrive before those expected from the TRN neurons, suggesting that local inhibition plays an early and significant role in the functioning of the somatosensory thalamus. KEY POINTS: The physiology and structure of local interneurons in the mouse somatosensory thalamus is described for the first time. Inhibitory interneurons have extensive dendritic arborization providing significant local dendro-dendritic inhibition in the somatosensory thalamus. Triadic and non-triadic synaptic connectivity onto thalamic relay neurons and other interneurons provides both local feedforward inhibition and disinhibition. Interneurons of the somatosensory thalamus provide inhibition before the thalamic reticular nucleus, suggesting they play an important role in sensory perception.
    MeSH term(s) Animals ; Interneurons ; Mice ; Neurons ; Thalamic Nuclei ; Thalamus
    Language English
    Publishing date 2021-10-23
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 3115-x
    ISSN 1469-7793 ; 0022-3751
    ISSN (online) 1469-7793
    ISSN 0022-3751
    DOI 10.1113/JP281711
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    In stock of ZB MED Cologne/Königswinter

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  3. Article ; Online: Correction: Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning.

    Denizdurduran, Berat / Markram, Henry / Gewaltig, Marc-Oliver

    Biological cybernetics

    2022  Volume 116, Issue 5-6, Page(s) 729

    Language English
    Publishing date 2022-10-18
    Publishing country Germany
    Document type Published Erratum
    ZDB-ID 220699-7
    ISSN 1432-0770 ; 0340-1200
    ISSN (online) 1432-0770
    ISSN 0340-1200
    DOI 10.1007/s00422-022-00949-2
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    Zs.A 1279: Show issues Location:
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  4. Article ; Online: Correction: Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning.

    Denizdurduran, Berat / Markram, Henry / Gewaltig, Marc-Oliver

    Biological cybernetics

    2022  Volume 116, Issue 5-6, Page(s) 727

    Language English
    Publishing date 2022-10-12
    Publishing country Germany
    Document type Published Erratum
    ZDB-ID 220699-7
    ISSN 1432-0770 ; 0340-1200
    ISSN (online) 1432-0770
    ISSN 0340-1200
    DOI 10.1007/s00422-022-00947-4
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    Zs.A 1279: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (1.OG)
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  5. Article ; Online: Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning.

    Denizdurduran, Berat / Markram, Henry / Gewaltig, Marc-Oliver

    Biological cybernetics

    2022  Volume 116, Issue 5-6, Page(s) 711–726

    Abstract: From the computational point of view, musculoskeletal control is the problem of controlling high degrees of freedom and dynamic multi-body system that is driven by redundant muscle units. A critical challenge in the control perspective of skeletal joints ...

    Abstract From the computational point of view, musculoskeletal control is the problem of controlling high degrees of freedom and dynamic multi-body system that is driven by redundant muscle units. A critical challenge in the control perspective of skeletal joints with antagonistic muscle pairs is finding methods robust to address this ill-posed nonlinear problem. To address this computational problem, we implemented a twofold optimization and learning framework to be specialized in addressing the redundancies in the muscle control . In the first part, we used model predictive control to obtain energy efficient skeletal trajectories to mimick human movements. The second part is to use deep reinforcement learning to obtain a sequence of stimulus to be given to muscles in order to obtain the skeletal trajectories with muscle control. We observed that the desired stimulus to muscles is only efficiently constructed by integrating the state and control input in a closed-loop setting as it resembles the proprioceptive integration in the spinal cord circuits. In this work, we showed how a variety of different reference trajectories can be obtained with optimal control and how these reference trajectories are mapped to the musculoskeletal control with deep reinforcement learning. Starting from the characteristics of human arm movement to obstacle avoidance experiment, our simulation results confirm the capabilities of our optimization and learning framework for a variety of dynamic movement trajectories. In summary, the proposed framework is offering a pipeline to complement the lack of experiments to record human motion-capture data as well as study the activation range of muscles to replicate the specific trajectory of interest. Using the trajectories from optimal control as a reference signal for reinforcement learning implementation has allowed us to acquire optimum and human-like behaviour of the musculoskeletal system which provides a framework to study human movement in-silico experiments. The present framework can also allow studying upper-arm rehabilitation with assistive robots given that one can use healthy subject movement recordings as reference to work on the control architecture of assistive robotics in order to compensate behavioural deficiencies. Hence, the framework opens to possibility of replicating or complementing labour-intensive, time-consuming and costly experiments with human subjects in the field of movement studies and digital twin of rehabilitation.
    MeSH term(s) Humans ; Movement/physiology ; Learning/physiology ; Reinforcement, Psychology ; Robotics/methods ; Musculoskeletal System
    Language English
    Publishing date 2022-08-11
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 220699-7
    ISSN 1432-0770 ; 0340-1200
    ISSN (online) 1432-0770
    ISSN 0340-1200
    DOI 10.1007/s00422-022-00940-x
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1279: Show issues Location:
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  6. Article: Reconstruction of the Hippocampus.

    Romani, Armando / Schürmann, Felix / Markram, Henry / Migliore, Michele

    Advances in experimental medicine and biology

    2022  Volume 1359, Page(s) 261–283

    Abstract: The hippocampus is a widely studied brain region thought to play an important role in higher cognitive functions such as learning, memory, and navigation. The amount of data on this region increases every day and delineates a complex and fragmented ... ...

    Abstract The hippocampus is a widely studied brain region thought to play an important role in higher cognitive functions such as learning, memory, and navigation. The amount of data on this region increases every day and delineates a complex and fragmented picture, but an integrated understanding of hippocampal function remains elusive. Computational methods can help to move the research forward, and reconstructing a full-scale model of the hippocampus is a challenging yet feasible task that the research community should undertake.In this chapter, we present strategies for reconstructing a large-scale model of the hippocampus. Based on a previously published approach to reconstruct and simulate brain tissue, which is also explained in Chap. 10 , we discuss the characteristics of the hippocampus in the light of its special anatomical and physiological features, data availability, and existing large-scale hippocampus models. A large-scale model of the hippocampus is a compound model of several building blocks: ion channels, morphologies, single cell models, connections, synapses. We discuss each of those building blocks separately and discuss how to merge them back and simulate the resulting network model.
    MeSH term(s) Brain ; Cognition/physiology ; Hippocampus/physiology ; Learning ; Synapses
    Language English
    Publishing date 2022-04-23
    Publishing country United States
    Document type Journal Article
    ISSN 2214-8019 ; 0065-2598
    ISSN (online) 2214-8019
    ISSN 0065-2598
    DOI 10.1007/978-3-030-89439-9_11
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  7. Article ; Online: Individual differences in sensory sensitivity: Further lessons from an Autism model.

    Favre, Mônica Regina / Markram, Henry / Markram, Kamila

    Cognitive neuroscience

    2019  Volume 10, Issue 3, Page(s) 171–173

    Abstract: In this commentary, we join Ward (this issue) in the usefulness of conceptualizing neural output in terms of signal and noise relationships, to create the missing links between neural, behavioral and subjective sensory sensitivity. We draw from our work ... ...

    Abstract In this commentary, we join Ward (this issue) in the usefulness of conceptualizing neural output in terms of signal and noise relationships, to create the missing links between neural, behavioral and subjective sensory sensitivity. We draw from our work in the Intense World Theory of Autism and the valproic acid rodent model, to complement the discussion with the consideration of developmental time and function of the system, for a neural output to serve as a predictor of atypical outcome in sensory sensitivity, and guide personalized therapies.
    MeSH term(s) Autism Spectrum Disorder ; Autistic Disorder ; Humans ; Individuality
    Language English
    Publishing date 2019-03-27
    Publishing country England
    Document type Journal Article ; Comment
    ZDB-ID 2542443-9
    ISSN 1758-8936 ; 1758-8928
    ISSN (online) 1758-8936
    ISSN 1758-8928
    DOI 10.1080/17588928.2019.1592143
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  8. Article ; Online: Seven challenges for neuroscience.

    Markram, Henry

    Functional neurology

    2013  Volume 28, Issue 3, Page(s) 145–151

    Abstract: Although twenty-first century neuroscience is a major scientific enterprise, advances in basic research have not yet translated into benefits for society. In this paper, I outline seven fundamental challenges that need to be overcome. First, neuroscience ...

    Abstract Although twenty-first century neuroscience is a major scientific enterprise, advances in basic research have not yet translated into benefits for society. In this paper, I outline seven fundamental challenges that need to be overcome. First, neuroscience has to become "big science" - we need big teams with the resources and competences to tackle the big problems. Second, we need to create interlinked sets of data providing a complete picture of single areas of the brain at their different levels of organization with "rungs" linking the descriptions for humans and other species. Such "data ladders" will help us to meet the third challenge - the development of efficient predictive tools, enabling us to drastically increase the information we can extract from expensive experiments. The fourth challenge goes one step further: we have to develop novel hardware and software sufficiently powerful to simulate the brain. In the future, supercomputer-based brain simulation will enable us to make in silico manipulations and recordings, which are currently completely impossible in the lab. The fifth and sixth challenges are translational. On the one hand we need to develop new ways of classifying and simulating brain disease, leading to better diagnosis and more effective drug discovery. On the other, we have to exploit our knowledge to build new brain-inspired technologies, with potentially huge benefits for industry and for society. This leads to the seventh challenge. Neuroscience can indeed deliver huge benefits but we have to be aware of widespread social concern about our work. We need to recognize the fears that exist, lay them to rest, and actively build public support for neuroscience research. We have to set goals for ourselves that the public can recognize and share. And then we have to deliver on our promises. Only in this way, will we receive the support and funding we need.
    MeSH term(s) Animals ; Brain/physiology ; Brain Diseases/classification ; Brain Diseases/physiopathology ; Computer Simulation ; Data Mining ; Databases, Factual ; Forecasting ; Humans ; Neurosciences/trends
    Language English
    Publishing date 2013-10-07
    Publishing country Italy
    Document type Journal Article ; Review
    ZDB-ID 645047-7
    ISSN 1971-3274 ; 0393-5264
    ISSN (online) 1971-3274
    ISSN 0393-5264
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2434: Show issues Location:
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  9. Article ; Online: Controlling morpho-electrophysiological variability of neurons with detailed biophysical models.

    Arnaudon, Alexis / Reva, Maria / Zbili, Mickael / Markram, Henry / Van Geit, Werner / Kanari, Lida

    iScience

    2023  Volume 26, Issue 11, Page(s) 108222

    Abstract: Variability, which is known to be a universal feature among biological units such as neuronal cells, holds significant importance, as, for example, it enables a robust encoding of a high volume of information in neuronal circuits and prevents ... ...

    Abstract Variability, which is known to be a universal feature among biological units such as neuronal cells, holds significant importance, as, for example, it enables a robust encoding of a high volume of information in neuronal circuits and prevents hypersynchronizations. While most computational studies on electrophysiological variability in neuronal circuits were done with single-compartment neuron models, we instead focus on the variability of detailed biophysical models of neuron multi-compartmental morphologies. We leverage a Markov chain Monte Carlo method to generate populations of electrical models reproducing the variability of experimental recordings while being compatible with a set of morphologies to faithfully represent specifi morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells and study the morpho-electrical variability and in particular, find that morphological variability alone is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.
    Language English
    Publishing date 2023-10-16
    Publishing country United States
    Document type Journal Article
    ISSN 2589-0042
    ISSN (online) 2589-0042
    DOI 10.1016/j.isci.2023.108222
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  10. Article: A Brief History of Simulation Neuroscience.

    Fan, Xue / Markram, Henry

    Frontiers in neuroinformatics

    2019  Volume 13, Page(s) 32

    Abstract: Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels ... ...

    Abstract Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain.
    Language English
    Publishing date 2019-05-07
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2452979-5
    ISSN 1662-5196
    ISSN 1662-5196
    DOI 10.3389/fninf.2019.00032
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