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14	14	**How the same network can generate different brain states with their specific propagation patterns and rhythms?**
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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.
	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.
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18		-[[image:example1.png]]
	18	+[[image:fig_live_poster_2021.png]]
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20		-[[image:example2.png]]
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22	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.
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24	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).
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