神经形态动态设备网络的噪声感知训练
Luca Manneschi1, Ian T Vidamour2, Kilian D Stenning3
1University of Sheffield, Sheffield, UK. l.manneschi@sheffield.ac.uk.
Nature communications
|October 16, 2025
概括
我们开发了一个噪音意识动态优化 (NADO) 框架,用于训练动态设备网络. 这种方法使得在复杂的系统中能够对体内智能进行强有力的编程,即使有设备噪声.
科学领域:
- 物理 物理学 物理
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 在物质计算中利用内在系统动态来实现体内智能.
- 设备网络可以实现复杂的任务,但很难动态设计.
- 设备噪音和缺乏物理模型阻碍了网络培训.
研究的目的:
- 引入动态设备培训网络的框架.
- 在复杂的系统中实现体现智能的强有力的编程.
- 解决设计具有固有的设备噪声的动态网络的挑战.
主要方法:
- 开发了噪声意识动态优化 (NADO) 框架.
- 利用神经静态微分方程 (神经-SDEs) 作为可微分的数字双胞胎.
- 结合了通过时间的反向传播和级联学习,以利用时间财产.
主要成果:
- 成功训练了用于时间分类和回归的自旋电子设备网络.
- 证明了对物理设备时间性质的有效利用.
- 展示了强大的,基于梯度的编程,没有分析设备描述.
结论:
- NADO框架可以有效地培训动态设备网络.
- 分离模型培训和网络优化减少了数据需求.
- 促进基于梯度的编程,用于复杂系统中的体现智能.
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