做梦改进了具有有限突触强度的Hopfield模型的记忆能力
Dreaming improves memorization in a Hopfield model with bounded synaptic strength
AI总结：
本文研究了在截断Hopfield模型中加入做梦阶段对记忆容量的影响，发现交替学习和做梦能提升记忆能力并使性能优化更现实。
摘要：
Hopfield模型为关联记忆提供了一个范式框架。其经典实现基于Hebbian学习规则，存在灾难性遗忘的问题：当试图存储太多模式时，网络无法检索任何模式。然而，Hebbian规则并未考虑突触强度是有限的。引入这种生物合理的修改，即“截断”，可以消除灾难性遗忘；模型现在能够检索最近看到的记忆，消除较旧的记忆。然而，其记忆容量相对于未截断的情况大大降低。本文研究了在截断Hopfield模型中加入“做梦”阶段对容量的影响。根据Hopfield、Feinstein和Palmer的提议，我们假设在做梦阶段，模型会生成随机模式，然后进行“反学习”。我们显示，尽管截断仍然可以消除灾难性遗忘，但交替进行学习和做梦阶段可以提高记忆容量，并使从进化角度寻找最佳性能变得更加现实。
英文摘要：
The Hopfield model provides a paradigmatic framework for associative memory. Its classical implementation, based on the Hebbian learning rule, suffers from catastrophic forgetting: when one attempts storing too many patterns, the network fails to retrieve any of them. Yet, the Hebbian rule does not take into account that synaptic strength is bounded. Introducing this biologically plausible modification, known as "clipping", eliminates catastrophic forgetting; the model is now able to retrieve the most recently seen memories, eliminating older ones. Yet, its memorization capacity is much reduced with respect to the unclipped case. Here, we investigate the effects of adding a "dreaming" phase on the capacity of a clipped Hopfield model. Following a proposal by Hopfield, Feinstein and Palmer, we assume that during the dreaming phase, the model generates random patterns that are then "unlearned". We show that while clipping still removes catastrophic forgetting, alternating learning and dreaming phases improves the memorization capacity and makes the search for optimal performance more realistic from an evolutionary perspective.
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