Content-addressable memory (CAM) networks, so-called because stored items can be recalled by partial or corrupted versions of the items, exhibit near-perfect recall of a small number of information-dense patterns below capacity and a `memory cliff' beyond, such that inserting a single additional pattern results in catastrophic forgetting of all stored patterns. We propose a novel ANN architecture, Memory Scaffold with Heteroassociation (MESH), that gracefully trades-off pattern richness with pattern number to generate a CAM continuum without a memory cliff: Small numbers of patterns are stored with complete information recovery matching standard CAMs, while inserting more patterns still results in partial recall of every pattern, with an information per pattern that scales inversely with the number of patterns. Motivated by the architecture of the Entorhinal-Hippocampal memory circuit in the brain, MESH is a tripartite architecture with pairwise interactions that uses a predetermined set of internally stabilized states together with heteroassociation between the internal states and arbitrary external patterns. We show analytically and experimentally that MESH nearly saturates the total information bound (given by the number of synapses) for CAM networks, invariant of the number of stored patterns, outperforming all existing CAM models.

提出了一种新型CAM体系结构——Memory Scaffold with Heteroassociation (MESH)，基于内部稳定态和外部信息关联，生成了一个没有存储限制的CAM连续体系，解决了存储模式数量与完整性之间的平衡问题，相较于现有的CAM模型表现更好。

通过异质联想固定支架实现无灾难性遗忘的内容可寻址存储器