受神经元振荡启发，实现无需监督的快速自适应学习
Rhythmic sharing: A bio-inspired paradigm for zero-shot adaptive learning in neural networks
- 通过连接权重振荡模拟神经节律，实现无监督自适应
- 可预测多种场景下的动态变化，包括从未见过的新场景
- 适用于主流AI模型，为认知建模提供新思路
大脑能快速适应新环境并从少量数据中学习，这是当前人工智能算法难以企及的能力。受神经细胞机械振荡节律的启发，我们提出一种基于连接强度振荡的学习范式，其中学习与振荡协调性相关。连接振荡可快速改变协调模式，使网络在无监督条件下感知并适应细微情境变化。该网络成为通用型AI架构，能够预测多种情境下的动态行为，包括未见情境。这一成果为新型认知模型提供了有力起点。由于该范式不依赖具体神经网络结构，本研究为将快速自适应学习引入主流AI模型开辟了道路。
原文摘要 · Abstract (English)
The brain rapidly adapts to new contexts and learns from limited data, a coveted characteristic that artificial intelligence (AI) algorithms struggle to mimic. Inspired by the mechanical oscillatory rhythms of neural cells, we developed a learning paradigm utilizing link strength oscillations, where learning is associated with the coordination of these oscillations. Link oscillations can rapidly change coordination, allowing the network to sense and adapt to subtle contextual changes without supervision. The network becomes a generalist AI architecture, capable of predicting dynamics of multiple contexts including unseen ones. These results make our paradigm a powerful starting point for novel models of cognition. Because our paradigm is agnostic to specifics of the neural network, our study opens doors for introducing rapid adaptive learning into leading AI models.
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