Criticality enhances the multilevel reliability of stimulus responses in cortical neural networks
Fig 1
Model architecture and dynamic modes: (A) Diagram of the recurrent excitation–inhibition network. External input rext has a background part r0 and an extra stimulus part r1. (B, C, E, F) Examples of dynamic modes for AS, Cri, SS, and P states. In each case, a period of the local field potential and the corresponding spike raster plot of Exc neurons, the distributions of the Pearson correlation coefficient (PCC) and coefficient of variance (CV) of inter-spike intervals, and the population activity autocorrelation (AC) are shown. (D) Examples of avalanche size distributions for AS, Cri, and SS states. Here, the background input strength is r0 = 0.8/ms, and synaptic parameters are for AS, Cri, SS, and P states, respectively.