情景记忆检测与检索特性的有效实现

Aniket Sharma, Pramod Kumar Singh, J. Prakash
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引用次数: 0

摘要

对大脑的深刻理解可以导致人工智能的重大突破。许多研究人员集中精力模拟人类思维,以便更好地理解其复杂性。为了更好地理解人类思维的情景记忆方面,我们提出了一个深度学习模型来实现人类情景记忆的检测和检索特性,情景记忆是长期记忆的一部分。采用Rosenblatt经验记忆模型的架构方法,提出了一种基于LSTM和CNN的记忆模型。将该方法的检测效率和准确率以及模型的检索性能与最近提出的方法进行了比较,证明了该方法的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Effective Implementation of Detection and Retrieval Property of Episodic Memory
A deep understanding of the brain can lead to significant breakthroughs in Artificial Intelligence. Many researchers concentrate their efforts on simulating the human mind to comprehend its complexities better. With the intention of better understanding the episodic memory aspect of the human mind, we propose a deep learning model to implement the detection and retrieval properties of human episodic memory, a part of long-term memory. A model based on LSTM and CNN is proposed, which follows the architectural methodology of Rosenblatt’s experiential memory model. A comparison of detection efficiency and accuracy and the proposed model’s retrieval property with a recently suggested method demonstrate its effectiveness and superiority.
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