Recurrent predictive coding models for associative memory employing covariance learning
Fig 3
Performance of covPCNs in AM of random patterns, and the equivalence between them.
A: A subset of 5 × 5 random patterns memorized by all 3 models. After training, we corrupted the bottom 2 rows (10 pixels) and let the networks run inference on the corrupted parts for retrieval. B: Retrieval MSEs of the models when corrupted with different mask sizes. Experiments in A and B are performed with networks with d = 25 neurons. C: Sample covariance of a random 2-dimensional dataset and the learned weight matrices of an explicit model and an implicit/dendritic model on this dataset. D: The random 2-dimensional dataset to memorize, and the linear retrieval obtained by masking the second dimension x2 by all 3 models, as well as the theoretical retrieval line. All the lines overlap as they are equivalent in theory. Experiments in C and D are performed with networks with d = 2 neurons.