Open the Lab link on the left to launch the interactive simulation

How the same network can generate different brain states with their specific propagation patterns and rhythms?

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.

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.

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).

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)

The predecessor of this model can be found at (3)

(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

(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

(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