论文标题
具有非常高内存率的关联记忆模型：通过顺序添加学习图像存储
An associative memory model with very high memory rate: Image storage by sequential addition learning
论文作者
论文摘要
在本文中，我们提出了与记忆和回忆有关的神经网络系统，该系统由一个神经元组（“提示球”）和一个单层神经网（“回忆网”）组成。该系统意识到提示球中的一个提示神经元与回忆网中的神经元之间的双向记忆学习。它可以记住许多模式，并回忆这些模式或任何时间相似的模式。此外，大多数时候都会召回模式。当召回一件事时，这种模型的召回情况几乎同时同时对人类的回忆类似于人类的回忆。也可以在系统中进行其他学习，而不会影响预先记忆的模式。此外，记忆率（记忆模式的数量 /神经元总数）接近100％；该系统的速率为0.987。最后，模式数据约束成为该系统的重要方面。
In this paper, we present a neural network system related to about memory and recall that consists of one neuron group (the "cue ball") and a one-layer neural net (the "recall net"). This system realizes the bidirectional memorization learning between one cue neuron in the cue ball and the neurons in the recall net. It can memorize many patterns and recall these patterns or those that are similar at any time. Furthermore, the patterns are recalled at most the same time. This model's recall situation seems to resemble human recall of a variety of similar things almost simultaneously when one thing is recalled. It is also possible for additional learning to occur in the system without affecting the patterns memorized in advance. Moreover, the memory rate (the number of memorized patterns / the total number of neurons) is close to 100%; this system's rate is 0.987. Finally, pattern data constraints become an important aspect of this system.
