Unambiguous reconstruction of network structure using avalanche dynamics

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
Timothée Leleu, Kazuyuki Aihara

Abstract

A robust method for inferring the structure of networks is presented based on the one-to-one correspondence between the expected composition of cascades of bursts of activity, called crackling noise or avalanches, and the weight matrix. Using a model of neuronal avalanches as a paradigmatic example, we derive this correspondence exactly by calculating the closed-form expression of the joint probability distribution of avalanche sizes obtained by counting separately the number of elements active in each subnetwork during avalanches.

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Citations

Mar 24, 2017·Neural Computation·Miho Itoh, Timothée Leleu
Jun 19, 2018·Network Neuroscience·Alexander ZhigalovJ Matias Palva

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