Network structure and subsampling

Inferring global properties of a system from observations is a challenge, even if one can observe the whole system. The same task becomes even more challenging if one can sample only a small number of the units at a time (spatial subsampling). For example, when recording from a brain area the spiking activity of only a very small fraction of all neurons can be accessed with millisecond precision. To still infer global properties, it is necessary to extrapolate from this small sampled fraction to the full system.

Selected presentations and publications:

T. Fardet, A. Levina (2021) Weighted directed clustering: interpretations and requirements for heterogeneous, inferred, and measured networks. Phys. Rev. Research 3.
Shi, D., Levina, A., & Noori, H. R. (2019). Refined parcellation of the nervous system by algorithmic detection of hidden features within communities. Physical Review E, 100(1), 1–14. https://doi.org/10.1103/PhysRevE.100.012301
Levina, A., & Priesemann, V. (2017). Subsampling scaling. Nature Communications, 8, 15140. https://doi.org/10.1038/ncomms15140

Subproject. Structural properties of neuronal networks

Even when studying "simple" neuronal networks in cultures, the connectivity patterns between neurons display highly non-random features that can significantly influence the network activity.

We use advanced simulation methods based on the DeNSE simulator and the NNGT graph library to study these properties and their impact on collective dynamics.

People involved:

Tanguy Fardet