This title appears in the Scientific Report :
2023
Please use the identifier:
http://hdl.handle.net/2128/34360 in citations.
Please use the identifier: http://dx.doi.org/10.1038/s42003-023-04580-0 in citations.
Simulations approaching data: cortical slow waves in inferred models of the whole hemisphere of mouse
Simulations approaching data: cortical slow waves in inferred models of the whole hemisphere of mouse
The development of novel techniques to record wide-field brain activity enables estimation of data-driven models from thousands of recording channels and hence across large regions of cortex. These in turn improve our understanding of the modulation of brain states and the richness of traveling wave...
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Personal Name(s): | Capone, Cristiano (Corresponding author) |
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De Luca, Chiara / De Bonis, Giulia / Gutzen, Robin / Bernava, Irene / Pastorelli, Elena / Simula, Francesco / Lupo, Cosimo / Tonielli, Leonardo / Resta, Francesco / Allegra Mascaro, Anna Letizia / Pavone, Francesco / Denker, Michael / Paolucci, Pier Stanislao | |
Contributing Institute: |
Computational and Systems Neuroscience; INM-6 Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 |
Published in: | Communications biology, 6 (2023) 1, S. 266 |
Imprint: |
London
Springer Nature
2023
|
DOI: |
10.1038/s42003-023-04580-0 |
Document Type: |
Journal Article |
Research Program: |
Human Brain Project Specific Grant Agreement 2 Human Brain Project Specific Grant Agreement 3 Digitization of Neuroscience and User-Community Building Neuroscientific Foundations |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1038/s42003-023-04580-0 in citations.
The development of novel techniques to record wide-field brain activity enables estimation of data-driven models from thousands of recording channels and hence across large regions of cortex. These in turn improve our understanding of the modulation of brain states and the richness of traveling waves dynamics. Here, we infer data-driven models from high-resolution in-vivo recordings of mouse brain obtained from wide-field calcium imaging. We then assimilate experimental and simulated data through the characterization of the spatio-temporal features of cortical waves in experimental recordings. Inference is built in two steps: an inner loop that optimizes a mean-field model by likelihood maximization, and an outer loop that optimizes a periodic neuro-modulation via direct comparison of observables that characterize cortical slow waves. The model reproduces most of the features of the non-stationary and non-linear dynamics present in the high-resolution in-vivo recordings of the mouse brain. The proposed approach offers new methods of characterizing and understanding cortical waves for experimental and computational neuroscientists. |