Baba Abdullatif
Computer Science and Engineering Dept., Kuwait College of Science and Technology, Doha Road, Kuwait City, Kuwait.
Computer Engineering Dept., The University of Turkish Aeronautical Association, Ankara, Türkiye.
Brain Inform. 2025 Aug 29;12(1):21. doi: 10.1186/s40708-025-00266-x.
This paper introduces the conceptual parallel between the ANN training process and the learning mechanisms of the human brain. Then, we briefly discuss a set of recently achieved experimental findings from a prior study that delves into various scenarios, aiding in comprehending the functionality of impaired or damaged neurons within a neural system. The key contribution of this paper is to present a novel variant of the Adam optimizer that incorporates a dynamic momentum adjustment factor, adaptive learning rate, and elastic weight consolidation technique. This enhanced version draws inspiration from biological processes to improve learning stability in artificial neural networks, with conceivable relevance to neural adaptation and rehabilitation research.
本文介绍了人工神经网络（ANN）训练过程与人类大脑学习机制之间的概念平行关系。然后，我们简要讨论了先前一项研究中最近取得的一组实验结果，该研究深入探讨了各种场景，有助于理解神经系统中受损神经元的功能。本文的关键贡献是提出了一种Adam优化器的新颖变体，它结合了动态动量调整因子、自适应学习率和弹性权重巩固技术。这个增强版本从生物过程中汲取灵感，以提高人工神经网络中的学习稳定性，这可能与神经适应和康复研究相关。
