Jones Alexander, Jha Rashmi
Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, United States.
Front Artif Intell. 2022 Aug 1;5:910407. doi: 10.3389/frai.2022.910407. eCollection 2022.
This work explores the process of adapting the segmented attractor network to a lifelong learning setting. Taking inspirations from Hopfield networks and content-addressable memory, the segmented attractor network is a powerful tool for associative memory applications. The network's performance as an associative memory is analyzed using multiple metrics. In addition to the network's general hit rate, its capability to recall unique memories and their frequency is also evaluated with respect to time. Finally, additional learning techniques are implemented to enhance the network's recall capacity in the application of lifelong learning. These learning techniques are based on human cognitive functions such as memory consolidation, prediction, and forgetting.
这项工作探索了将分段吸引子网络应用于终身学习环境的过程。受霍普菲尔德网络和内容可寻址存储器的启发，分段吸引子网络是用于联想记忆应用的强大工具。使用多种指标分析了该网络作为联想记忆的性能。除了网络的一般命中率外，还针对时间评估了其回忆独特记忆的能力及其频率。最后，实施了额外的学习技术，以提高网络在终身学习应用中的回忆能力。这些学习技术基于人类认知功能，如记忆巩固、预测和遗忘。
