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 loss of all stored patterns. We propose a novel CAM
architecture, Memory Scaffold with Heteroassociation (MESH), that factorizes
the problems of internal attractor dynamics and association with external
content 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 a graceful trade-off between pattern number and pattern richness.
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 for any number of stored patterns,
MESH nearly saturates the total information bound (given by the number of
synapses) for CAM networks, 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

Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold

Quantum computing allows for the potential of significant advancements in
both the speed and the capacity of widely used machine learning techniques.
Here we employ quantum algorithms for the Hopfield network, which can be used
for pattern recognition, reconstruction, and optimization as a realization of a
content-addressable memory system. We show that an exponentially large network
can be stored in a polynomial number of quantum bits by encoding the network
into the amplitudes of quantum states. By introducing a classical technique for
operating the Hopfield network, we can leverage quantum algorithms to obtain a
quantum computational complexity that is logarithmic in the dimension of the
data. We also present an application of our method as a genetic sequence
recognizer.

采用量子算法进行 Hopfield 网络的内容寻址存储，并将其应用于基因序列识别。

量子 Hopfield 神经网络

Quantum Hopfield neural network

The long-term memory of most connectionist systems lies entirely in the
weights of the system. Since the number of weights is typically fixed, this
bounds the total amount of knowledge that can be learned and stored. Though
this is not normally a problem for a neural network designed for a specific
task, such a bound is undesirable for a system that continually learns over an
open range of domains. To address this, we describe a lifelong learning system
that leverages a fast, though non-differentiable, content-addressable memory
which can be exploited to encode both a long history of sequential episodic
knowledge and semantic knowledge over many episodes for an unbounded number of
domains. This opens the door for investigation into transfer learning, and
leveraging prior knowledge that has been learned over a lifetime of experiences
to new domains.

本文介绍了一种利用快速但不可微的内容可寻址内存编码长序列经验和跨多个领域的语义知识的终身学习系统，从而在不受限制的领域上不断学习并存储知识，打开了探索迁移学习和利用过去学习经验到新领域的机会。