AI 中文总结
针对物联网边缘智能处理受微控制器限制问题，提出基于接收神经元模型的神经形态启发式分类器，能实现非线性可分决策边界，可直接部署在中端微控制器上，实验结果表明其是资源受限神经形态边缘系统的可行替代方案。
AI 中文摘要
物联网网络边缘对智能处理的需求不断增长，但受微控制器单元严重的计算和内存限制，传统深度学习方法难以应用。我们提出一种基于接收神经元模型的神经形态启发式分类器，该模型为单单元架构，能实现非线性可分决策边界，无需多层网络。它专为在中端微控制器上直接部署设计，支持持续的设备端自适应。在基本数据集基准上的实验评估得出与标准机器学习方法基线兼容的交叉验证准确率。这些结果表明接收神经元模型是在动态、非平稳环境中运行的资源受限神经形态边缘系统的可行且可解释的替代方案。
英文摘要
The growing demand for intelligent processing at the edge of IoT networks is constrained by the severe computational and memory limitations of microcontroller units, which render impractical conventional deep learning approaches. We propose a neuromorphicinspired classifier based on the Receptron model, a single-unit architecture capable of implementing non-linearly separable decision boundaries, without resorting to multi-layer networks. The model is designed for direct deployment on mid-range MCUs, while supporting continuous on-device adaptation. Experimental evaluation on basic dataset benchmarks yields cross-validated accuracies compatible with standard machine learning method baselines. These results position the Receptron as a viable and interpretable alternative for resource-constrained neuromorphic edge systems operating in dynamic, non-stationary environments.
CommentsThis work has been submitted to NeuroPHY 2026
