Drawing from memory the face of a friend you have not seen in years is a
difficult task. However, if you happen to cross paths, you would easily
recognize each other. The biological memory is equipped with an impressive
compression algorithm that can store the essential, and then infer the details
to match perception. The Willshaw Memory is a simple abstract model for
cortical computations which implements mechanisms of biological memories. Using
our recently proposed sparse coding prescription for visual patterns [34], this
model can store and retrieve an impressive amount of real-world data in a
fault-tolerant manner. In this paper, we extend the capabilities of the basic
Associative Memory Model by using a Multiple-Modality framework. In this
setting, the memory stores several modalities (e.g., visual, or textual) of
each pattern simultaneously. After training, the memory can be used to infer
missing modalities when just a subset is perceived. Using a simple
encoder-memory decoder architecture, and a newly proposed iterative retrieval
algorithm for the Willshaw Model, we perform experiments on the MNIST dataset.
By storing both the images and labels as modalities, a single Memory can be
used not only to retrieve and complete patterns but also to classify and
generate new ones. We further discuss how this model could be used for other
learning tasks, thus serving as a biologically-inspired framework for learning.

使用 Willshaw Memory 模型和 Multiple-Modality 框架实现稀疏编码，能够储存和检索大量的现实数据，并且支持多模态同步存储与检索以及缺失模式进行推理，该框架可用于其他的学习任务。