Emergence of Slow-Switching Assemblies in Structured Neuronal Networks
Fig 2
The relationship of observed SSA dynamics with the structural connectivity clustering of the LIF network, REE, and the spectral gap, Δλ.
The presence of SSA dynamics is quantified through the spike-rate variability metric (), which measures the variance of the firing rates of the assemblies normalized by a randomly shuffled bootstrap: increases with increasing SSA activity and for completely asynchronous activity (as in the unclustered case in Fig 1A) (see Materials and Methods). A Spike-rate variability is plotted as a function of REE (left) and Δλ (right) for different network sizes (dots: raw data from simulations; line: mean; shading: standard deviation). Above a certain clustering threshold, SSA emerges and increases as REE grows; the intensity of the SSA dynamics is in line with the presence of an eigenvalue gap Δλ in the weight matrix. B Spike-rate variability as a function of REE (left) and Δλ (right) for a network of 2000 neurons with different numbers of clusters, yielding qualitatively similar results (dots: raw data from simulations; line: mean; shading: standard deviation). C Relationship between the clustering strength REE and the spectral gap Δλ. Observe that REE is not sufficient to determine Δλ, i.e. Δλ is influenced by other aspects such as the network size and number of groups.