Last modified by pierstanpaolucci on 2023/06/29 18:29
From version 25.1
edited by cristianocapone
on 2021/09/23 13:36
Change comment: There is no comment for this version
To version 7.1
edited by cristianocapone
on 2021/09/21 12:39
Change comment: There is no comment for this version

Summary

Details

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2 2  (((
3 3  (% class="container" %)
4 4  (((
5 -(% class="lead" id="HInteractiveExplorationofBrainStatesandSpatio-TemporalActivityPatternsinData-ConstrainedSimulations" %)
6 -Open the Lab link on the left to explore brain states and spatio-temporal cortical activity patterns on your own
5 += Interactive Exploration of Brain States and Spatio-Temporal Activity Patterns in Data-Constrained Simulations =
6 +
7 += =
8 +
9 +Explore brain states and spatio-temporal cortical activity patterns on your own
7 7  )))
8 8  )))
9 9  
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12 12  (% class="col-xs-12 col-sm-8" %)
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14 -**How the same network can generate different brain states with their specific propagation patterns and rhythms?**
17 += What can I find here? =
15 15  
16 -In this Jupyter Lab environment, 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.
19 +* Notice how the table of contents on the right
20 +* is automatically updated
21 +* to hold this page's headers
17 17  
18 -[[image:example1.png]]
23 += Who has access? =
19 19  
20 -[[image:example2.png]]
21 -
22 -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.
23 -
24 -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).
25 -
26 -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).
27 -
28 -The predecessor of this model can be found at (3).
29 -
30 -The latest version of the code presented in the drive of this collab can be found at (4).
31 -
32 -(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]]
33 -
34 -(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]]
35 -
36 -(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]]
37 -
38 -(% class="wikigeneratedid" id="H" %)
39 -(4) [[https:~~/~~/github.com/APE-group/InteractiveExplorationBrainStates>>https://github.com/APE-group/InteractiveExplorationBrainStates]]
25 +Describe the audience of this collab.
40 40  )))
41 41  
28 +
42 42  (% class="col-xs-12 col-sm-4" %)
43 43  (((
44 44  {{box title="**Contents**"}}
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45 45  {{toc/}}
46 46  {{/box}}
47 47  
48 -
49 -
50 -
51 51  
52 52  )))
53 53  )))
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