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异源联想记忆检索中的缺失线索问题。

The missing cue problem in hetero associative memory retrieval.

作者信息

Morales Rafael, Pineda Luis A

机构信息

Universidad de Guadalajara, SUV, Guadalajara, 44130, Mexico.

Universidad Nacional Autónoma de México, IIMAS, Mexico City, 04510, Mexico.

出版信息

Sci Rep. 2025 Jul 1;15(1):21850. doi: 10.1038/s41598-025-07963-x.

Abstract

The Entropic Associative Memory is an auto-associative memory in which objects are represented and stored as discrete functions or "traces" in a table, so the memory content is a 2D relation or "memory plane". Memory traces are "overlapped" on the medium, the memory is indeterminate, and the system is entropic. The memory retrieval operation produces an object out of a cue and the indeterminate memory mass, and the memory is constructive. In this paper, we present its extension to the hetero-associative case. Pairs of hetero-associated objects, possibly of different domain and/or modalities, are held in a 4D relation. The cue to a memory retrieval operation selects a largely indeterminate 2D hetero-associated memory plane, but there is no cue left to recover the object from such plane. We propose three incremental methods to address such missing cue problem, which we call random, sample and test, and search and test. The model is assessed with composite recollections consisting of manuscript digits and letters selected from the MNIST and EMNIST corpora, respectively, such that cue digits retrieve their associated letters and vice versa. We show the memory performance and illustrate the memory retrieval operation using all three methods. The system shows promise for storing, recognizing, and retrieving very large sets of objects with very limited computing resources. We also discuss the implications of the model for the psychology and the neuroscience of memory.

摘要

熵联想记忆是一种自联想记忆,其中对象以离散函数或“痕迹”的形式表示并存储在一个表中,因此记忆内容是一种二维关系或“记忆平面”。记忆痕迹在介质上“重叠”,记忆是不确定的,并且系统是熵性的。记忆检索操作从一个线索和不确定的记忆体中产生一个对象,并且记忆是建设性的。在本文中,我们展示了它向异联想情况的扩展。可能具有不同域和/或模态的异联想对象对以四维关系保存。记忆检索操作的线索选择一个很大程度上不确定的二维异联想记忆平面,但没有线索可用于从这样的平面中恢复对象。我们提出了三种增量方法来解决这种缺失线索的问题,我们称之为随机、采样与测试以及搜索与测试。该模型使用分别从MNIST和EMNIST语料库中选取的手写数字和字母组成的复合回忆进行评估,使得线索数字检索其相关联的字母,反之亦然。我们展示了记忆性能,并使用所有三种方法说明了记忆检索操作。该系统显示出在非常有限的计算资源下存储、识别和检索非常大的对象集的潜力。我们还讨论了该模型对记忆心理学和神经科学的意义。

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