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From version 6.1
edited by cristianocapone
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edited by cristianocapone
on 2021/09/22 10:57
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Summary

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1 -Interactive Exploration of Brain States and Spatio Temporal Activity Patterns in Data-Constrained Simulations
1 +Interactive Exploration of Brain States and Spatio-Temporal Activity Patterns in Data-Constrained Simulations
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2 2  (((
3 3  (% class="container" %)
4 4  (((
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
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
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11 11  )))
12 12  
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14 14  (((
15 15  (% class="col-xs-12 col-sm-8" %)
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17 -= What can I find here? =
14 +**How the same network can generate different brain states with their specific propagation patterns and rhythms?**
18 18  
19 -* Notice how the table of contents on the right
20 -* is automatically updated
21 -* to hold this page's headers
16 +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.
22 22  
23 -= Who has access? =
18 +[[image:example1.png]]
24 24  
25 -Describe the audience of this collab.
26 -)))
20 +[[image:example2.png]]
27 27  
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.
28 28  
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]]
40 +)))
41 +
29 29  (% class="col-xs-12 col-sm-4" %)
30 30  (((
31 31  {{box title="**Contents**"}}
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32 32  {{toc/}}
33 33  {{/box}}
34 34  
48 +
49 +
50 +
35 35  
36 36  )))
37 37  )))
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