Selective consistency of recurrent neural networks induced by plasticity as a mechanism of unsupervised perceptual learning
Fig 6
The evaluation of selective consistency without optimizing the readout weights.
The figure styles are the same as Fig 4.The consistency was evaluated by the correlation between the first and second segment time series for each test run for repeated noise (RN; left) and referenced repeated noise (RefRN; right). The violin plots show probability density distributions and interquartile ranges of Hebbian (right side; magenta and brown) and non–Hebbian (left side; green and cyan) models, respectively (****; PR < 0.01%, p < 0.001). The colored line plots connect the mean values for each condition. The black lines in the bottom windows show the difference between Hebbian and non–Hebbian models. The horizontal axis represents the spectral radius of the evaluated networks.