  Associative memories in the brain receive and store patterns of activity
registered by the sensory neurons, and are able to retrieve them when
necessary. Due to their importance in human intelligence, computational models
of associative memories have been developed for several decades now. They
include autoassociative memories, which allow for storing data points and
retrieving a stored data point $s$ when provided with a noisy or partial
variant of $s$, and heteroassociative memories, able to store and recall
multi-modal data. In this paper, we present a novel neural model for realizing
associative memories, based on a hierarchical generative network that receives
external stimuli via sensory neurons. This model is trained using predictive
coding, an error-based learning algorithm inspired by information processing in
the cortex. To test the capabilities of this model, we perform multiple
retrieval experiments from both corrupted and incomplete data points. In an
extensive comparison, we show that this new model outperforms in retrieval
accuracy and robustness popular associative memory models, such as autoencoders
trained via backpropagation, and modern Hopfield networks. In particular, in
completing partial data points, our model achieves remarkable results on
natural image datasets, such as ImageNet, with a surprisingly high accuracy,
even when only a tiny fraction of pixels of the original images is presented.
Furthermore, we show that this method is able to handle multi-modal data,
retrieving images from descriptions, and vice versa. We conclude by discussing
the possible impact of this work in the neuroscience community, by showing that
our model provides a plausible framework to study learning and retrieval of
memories in the brain, as it closely mimics the behavior of the hippocampus as
a memory index and generative model.
