Fig. 1
The spatially extended, conductance-based spiking neural network generates co-activated patterns with critical dynamics. a The snapshot of spontaneous activity shows co-activated patterns in our circuit model (\(250\times 250\) neurons). The magenta dots represent spikes at this moment, illustrating that the fraction of individual neurons participating in a wave is low (\(2\pm 0.4\)%, mean\(\pm\)s.d., s.d. represents standard deviation). As these localized activity patterns move in a complex way, all areas of the circuit would be visited by them. b An illustrative example of three cascades (dots represent spikes and the color encodes time). For each time step, connected components of spikes are clustered within a radius \({r}_{{\rm{S}}}\). A cascade is defined as a set of clustered spike objects whose center of mass changes by less than \({r}_{{\rm{T}}}\) between successive time (\({T}_{2}-{T}_{1}\,=\,1\, {\mathrm{ms}}\)). The interaction between cascade 1 and 2 destroys cascade 1. c Distribution of cascade sizes follows a power-law function, \(P(S) \sim {S}^{-\tau }\). Inset: the complementary cumulative distribution function \(P(S\ge s)\) of the same data also follows a power-law function. d Same as in c but for cascade durations, \(P(D) \sim {D}^{-\alpha }\). e Top: raster plot shows the spike times of a sub-population of randomly selected 70 excitatory neurons in 3 s. Bottom: single-neuron spike count of a randomly chosen neuron (250 ms bin, sliding over in 10-ms step). f Fano factor of spontaneous activity as a function of time window \(\Delta t\) during the spontaneous activity and after stimulation. g Spike-triggered averaged (STA) membrane potential (\({V}_{{\rm{m}}}\)) during 2 s shows multiple correlated patterns. The black circle labels the seed neuron. Source data are provided as a Source Data file
