Rudroff Thorsten, Rainio Oona, Klén Riku
Turku PET Centre, University of Turku and Turku University Hospital, 20520 Turku, Finland.
Brain Sci. 2024 Oct 31;14(11):1111. doi: 10.3390/brainsci14111111.
The stability-plasticity dilemma remains a critical challenge in developing artificial intelligence (AI) systems capable of continuous learning. This perspective paper presents a novel approach by drawing inspiration from the mammalian hippocampus-cortex system. We elucidate how this biological system's ability to balance rapid learning with long-term memory retention can inspire novel AI architectures. Our analysis focuses on key mechanisms, including complementary learning systems and memory consolidation, with emphasis on recent discoveries about sharp-wave ripples and barrages of action potentials. We propose innovative AI designs incorporating dual learning rates, offline consolidation, and dynamic plasticity modulation. This interdisciplinary approach offers a framework for more adaptive AI systems while providing insights into biological learning. We present testable predictions and discuss potential implementations and implications of these biologically inspired principles. By bridging neuroscience and AI, our perspective aims to catalyze advancements in both fields, potentially revolutionizing AI capabilities while deepening our understanding of neural processes.
在开发能够持续学习的人工智能（AI）系统方面，稳定性与可塑性的两难问题仍然是一个关键挑战。这篇观点论文从哺乳动物海马体-皮质系统中汲取灵感，提出了一种新颖的方法。我们阐明了这个生物系统如何在快速学习与长期记忆保持之间取得平衡，进而为新型AI架构提供启发。我们的分析聚焦于关键机制，包括互补学习系统和记忆巩固，尤其强调了关于尖波涟漪和动作电位爆发的最新发现。我们提出了融合双学习率、离线巩固和动态可塑性调制的创新型AI设计。这种跨学科方法为更具适应性的AI系统提供了一个框架，同时也为生物学习提供了见解。我们提出了可测试的预测，并讨论了这些受生物学启发的原理的潜在实现方式和影响。通过将神经科学与AI相结合，我们的观点旨在推动这两个领域的进步，有可能彻底改变AI的能力，同时加深我们对神经过程的理解。
