Selective consistency of recurrent neural networks induced by plasticity as a mechanism of unsupervised perceptual learning
Fig 5
The edge of chaos and selective consistency.
Each bin of the histograms shows averaged inter–segment correlation for four conditions (magenta; RN of Hebbian network, green; RN of non–Hebbian network, brown; RefRN of Hebbian network, cyan; RefRN of non–Hebbian network). The histograms represent results from networks with spectral radii of 0.9, 1.4, and 1.9, from left to right—which can be described as stable, edge of chaos, and chaotic, respectively (****; PR < 0.01%, p < 0.001). The error bars are representing 95% confidence intervals. Notably, the edge of chaos region was chosen for its strong observation of selective consistency (see Fig 4). The statistical significances were tested with the nonparametric rank–order test based on the surrogate data model technique. It was found that in networks that are either stable or conversely chaotic, there was no difference between conditions, and differences were observed only in the edge of chaos region.