On the prediction of neuronal microscale topology descriptors based on mesoscale recordings.

ABSTRACT: The brain possesses structural and functional hierarchical architectures organized over multiple scales. Considering that functional recordings commonly focused on a single spatial level, and because multiple scales interact with one another, we explored the behaviour of in silico neuronal networks across different scales. We established ad hoc relations of several topological descriptors (average clustering coefficient, average path length, small-world propensity, modularity, network degree, synchronizability and fraction of long-term connections) between different scales upon application and empirical validation of a Euclidian renormalization approach. We tested a simple network (distance-dependent model) as well as an artificial cortical network (Vertex; undirected and directed networks

SUBMITTER: Bonzanni M 

PROVIDER: S-EPMC9209896 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature