Last modified by pierstanpaolucci on 2023/06/29 18:29
From version 10.1
edited by pierstanpaolucci
on 2021/09/21 15:38
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To version 18.1
edited by pierstanpaolucci
on 2021/09/22 10:49
Change comment: There is no comment for this version

Summary

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5 -= Interactive Exploration of Brain States and Spatio-Temporal Activity Patterns in Data-Constrained Simulations =
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9 -Explore brain states and spatio-temporal cortical activity patterns on your own
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6 +Open the Lab link on the left to explore brain states and spatio-temporal cortical activity patterns on your own
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17 -= Open the Lab link on the left to launch the interactive simulation =
14 +**How the same network can generate different brain states with their specific propagation patterns and rhythms?**
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19 -How the same network can generate different brain states with their specific propagation patterns and rhythms?
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21 21  In this Jupyter Lab the user can interactively change the neuromodulated fatigue parameters and observe in real-time the emergence of different categories of slow- wave wave-propagation patterns and the transition to an asynchronous regime on a columnar mean-field model equipped with lateral connections inferred from experimentally acquired cortical activity.
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23 23  The model displays the dorsal view of a mouse cortical hemisphere sampled by pixels of 100-micron size over a 25 mm2 field of view.
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25 25  The connectivity of the model was inferred from cortical activity acquired using GECI imaging technique. Even if the connectivity of the model was inferred from a single brain-state, the neuromodulated model supports the emergence of a rich dynamic repertoire of spatio-temporal propagation patterns, from those corresponding to deepests levels of anesthesia (spirals) to classical postero-anterior and rostro-caudal waves up to the transition to asynchronous activity, with the dissolution of the slow-wave features (1).
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27 -The experimental data set from which the model has been inferred has been provided by LENS and it is available in the EBRAINS KG(2)
22 +The experimental data set from which the model has been inferred has been provided by LENS and it is available in the EBRAINS KG (2)
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29 -(1) Capone, C. et al. (2021) “Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse” arXiv:2104.07445 https:~/~/arxiv.org/abs/2104.07445
24 +The predecessor of this model can be found at (3)
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26 +The latest version of the code presented in the drive of this collab can be found at (4)
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28 +(1) Capone, C. et al. (2021) “Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse” arXiv:2104.07445 [[https:~~/~~/arxiv.org/abs/2104.07445>>https://arxiv.org/abs/2104.07445]]
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30 +(2) Resta, F., Allegra Mascaro, A. L., & Pavone, F. (2020). //Study of Slow Waves (SWs) propagation through wide-field calcium imaging of the right cortical hemisphere of GCaMP6f mice// [Data set]. EBRAINS. [[DOI: 10.25493/3E6Y-E8G>>url:https://doi.org/10.25493%2F3E6Y-E8G]]
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32 +(3) Mean Field Simulation of whole mouse hemisphere with parameters inferred from optical recordings [[https:~~/~~/search.kg.ebrains.eu/instances/e572362f-9461-4f9d-81e2-b69cd44185f4>>https://search.kg.ebrains.eu/instances/e572362f-9461-4f9d-81e2-b69cd44185f4]]
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35 +(4) [[https:~~/~~/github.com/APE-group/InteractiveExplorationBrainStates>>https://github.com/APE-group/InteractiveExplorationBrainStates]]
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37 37  {{box title="**Contents**"}}