Recurrent predictive coding models for associative memory employing covariance learning
Fig 6
Comparison of linear and nonlinear implicit covPCN.
A: Performance of linear and nonlinear implicit models in the completion task with varying Ns. B: Same as A, but with the denoising task, where cues are memories with Gaussian noise of variance 0.1. C: A simple 3-dimensional example, where stars are data points the networks were trained to memorize. After training we ran inference on both linear and nonlinear models, initialized with grid test data drawn from the range [−1, 1]3. The position of the test data at convergence of inference indicates the shape of attractors. Images taken from the CIFAR10 dataset [20].