Cyclic transitions between higher order motifs underlie sustained asynchronous spiking in sparse recurrent networks
Fig 10
Unclustered (Erdős-Renyi) networks.
A: Left, density (ratio of existing to possible connections) for synaptic, functional, and recruitment ER graphs. Right, reciprocity (ratio of reciprocal to all existing connections) for synaptic, functional, and recruitment ER graphs. B: Clustering propensity for isomorphic triangle motifs on ER graph simulations. The y-axis is scaled to match that of Fig 7A (clustering propensities on original graphs) and Fig 9B (clustering propensities on graphs with 1.5 times increased weights). C: Probabilities of dominance of each triangle motif. The dominant motif at a time point is given by the maximum of mean middleman, mean fan-in, and mean fan-out across units. D: Second order motif state probabilities for progression of temporal recruitment graphs. E: Probabilities for each motif to follow a given second order motif. F: Hitting times for each state for the Markov process defined by motif transition probabilities. G: Trajectories of all runs on a sample ER network in 3-dimensional isomorphic motif space. All runs reached completion. H: Markov Matrix for transition probabilities between motifs.