Selective consistency of recurrent neural networks induced by plasticity as a mechanism of unsupervised perceptual learning
Fig 2
(a) The process of input signal formation. There are four stimuli types: N and RefN stimuli consist of 1 s of white noise, whereas RN and RefRN stimuli consist of 0.5 s repetitions of white noise. The sampling frequency is 44 kHz. Subsequently, each time series is passed through an A–weighting filter, which reflects human auditory characteristics, peaking around 3,000 Hz, with high frequencies attenuated. The middle figure shows the resulting power spectra of before (gray) and after (black) A–weighting filter used in the simulation. After filter adaptation, each stimulus was resampled at 2,000 Hz to reduce computational costs. (b) An overview of the model. The resampled time series are presented to the neuron in the input layer as stimuli. W, the reservoir weights matrix is dynamic and maintained by Oja’s Hebbian plasticity rule. Wout, the weights between the reservoir and neurons in the output layer are optimized using the gradient descent method. The model’s output target is one step ahead of prediction of the input time series.