受大脑神经可塑性启发，提出动态增删神经元的AI学习新范式。
Neuroplasticity in Artificial Intelligence -- An Overview and Inspirations on Drop In & Out Learning
- 用'插接入'模拟神经发生，'丢弃'与剪枝模拟神经凋亡
- 主张通过结构动态调整实现持续学习，突破静态模型局限
- 适合研究终身学习、类脑计算的学者参考
人工智能在深度神经网络（DNN）推动下达到新高度并广泛普及。尽管早期受人脑神经元启发，但现有架构普遍忽略大脑中神经发生、神经可塑性及神经凋亡等关键过程。当前AI发展聚焦于大型语言模型等静态结构框架，训练与推理阶段连接固定。本文探讨神经发生、神经凋亡与神经可塑性对AI的启示，提出在人工神经网络中引入'插接入'（dropin）模拟神经发生，重访'丢弃'（dropout）与结构剪枝模拟神经凋亡。进一步建议将二者结合，构建面向终身学习场景的动态神经网络，以实现生物启发式的持续适应能力。最后呼吁加强跨学科研究，并指明未来探索方向。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) has achieved new levels of performance and spread in public usage with the rise of deep neural networks (DNNs). Initially inspired by human neurons and their connections, NNs have become the foundation of AI models for many advanced architectures. However, some of the most integral processes in the human brain, particularly neurogenesis and neuroplasticity in addition to the more spread neuroapoptosis have largely been ignored in DNN architecture design. Instead, contemporary AI development predominantly focuses on constructing advanced frameworks, such as large language models, which retain a static structure of neural connections during training and inference. In this light, we explore how neurogenesis, neuroapoptosis, and neuroplasticity can inspire future AI advances. Specifically, we examine analogous activities in artificial NNs, introducing the concepts of ``dropin'' for neurogenesis and revisiting ``dropout'' and structural pruning for neuroapoptosis. We additionally suggest neuroplasticity combining the two for future large NNs in ``life-long learning'' settings following the biological inspiration. We conclude by advocating for greater research efforts in this interdisciplinary domain and identifying promising directions for future exploration.
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