Wiki source code of Showcase 3 Brain Complexity and Consciousness
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24.1 | 5 | = [[image:image-20230315100457-1.png||height="196" width="464"]] = |
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29.1 | 7 | == **Showcase 3: **(% style="color:#000000" %)Brain Complexity and Consciousnesss(%%) == |
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25.1 | 16 | == **Introduction** == |
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| 19 | **Understanding consciousness is one of the grand challenges of contemporary neuroscience.** | ||
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10.1 | 21 | * Why does it fade and recover during transitions across physiological, pharmacological and pathological brain states? |
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15.1 | 22 | * How can we determine whether a behaviorally unresponsive patient is conscious? |
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10.1 | 23 | * Can we quantify consciousness levels? |
| 24 | * Can we use our multi-scale understanding of brain-state transitions to devise strategies to induce recovery of consciousness? | ||
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| 27 | A brain-based quantification of the levels of consciousness is of the utmost importance because, each year, intensive care medicine is called upon to treat millions of patients whose level of consciousness is difficult to assess due to severe brain injuries and disconnections. Detecting the fundamental mechanisms of consciousness is crucial, not only for better diagnosis, but also to guide recovery in an optimal manner. | ||
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15.1 | 30 | It is also critical to provide tools - such as eye tracking or brain computer interfaces - to provide communication channels for patients who have recovered consciousness but remain disconnected (e.g. locked-in patients). Another relevant requirement comes from the field of anesthesiology - pharmacologically induced alterations of consciousness – which is used in millions of patients every year. The effectiveness of this approach is limited by a lack of systematic understanding of the underlying circuit mechanisms and a lack of reliable brain-based measures of anesthesia depth. Therefore, deeper understanding of consciousness also paves the way to engineering novel methods of tracking the results of pharmacological interventions, as well as engineering next-generation, non-pharmaceutical, direct methods for inducing states of non-responsiveness, with potentially fewer side effects and dangers. |
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15.1 | 33 | Here we demonstrate that the implementation of AdEx mean-field models into TVB leads to a framework where one can evaluate the effect of “microscopic” parameters, such as spike-frequency adaptation, on the “macroscopic” behavior at the level of the whole brain, such as the emergence of: |
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| 35 | |**ASYNCHRONOUS DYNAMICS (wake-like)**|**SYNCHRONIZED SLOW-WAVES (sleep-like)** | ||
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20.1 | 36 | |[[image:image-20230315095754-3.png]]|[[image:image-20230315095658-2.png]] |
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25.1 | 38 | = **What can you find in this Collab?** = |
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15.1 | 40 | This Collaboratory provides three Python notebooks that can be executed within the lab.ebrains.eu environment. |
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| 42 | Some small GUIs are embedded in the notebooks to facilitate the execution of them. There, you will be able to configure all the different parameters of the simulation, execute the simulations and visualize the obtained results. | ||
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| 46 | We highly recommend cloning this Public Collab to a private Collab of your own. To do so, please follow these instructions: | ||
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34.1 | 47 | \\1) Go to [[lab.ebrains.eu>>lab.ebrains.eu]] and find the current Public Collab "Showcase 3 Brain Complexity and Consciousness" |
| 48 | \\2) Open the notebook "copy_showcase3_collab.ipynb" and execute the unique cell available in the notebook. | ||
| 49 | \\3) After 4-5 minutes, the new private Collab will be created and the corresponding Collab URL link will be returned as the output of the Cell. | ||
| 50 | \\4) You can now open the link and start interacting with the Showcase 3 Notebooks | ||
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