Abstract A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate. Multiple systems have recently implemented some or all of these operations, yet the focus was placed on addressing technological challenges. Fundamental questions regarding learning in hardware neural networks remain largely unexplored. Noise in particular is unavoidable in such architectures, and here we experimentally and theoretically investigate its interaction with a learning algorithm using an opto-electronic recurrent neural network. We find that noise strongly modifies the system’s path during convergence, and surprisingly fully decorrelates the final readout weight matrices. This highlights the importance of understanding architecture, noise and learning algorithm as interacting players, and therefore identifies the need for mathematical tools for noisy, analogue system optimization.

中文翻译：

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硬件神经网络中噪声扰动下的布尔学习

摘要 神经网络的高效硬件集成得益于在物理基础上完全实现非线性、网络连通性和学习。多个系统最近实施了部分或全部这些操作，但重点是解决技术挑战。关于硬件神经网络学习的基本问题在很大程度上仍未得到探索。在这种架构中，噪声尤其是不可避免的，在这里我们通过实验和理论研究了噪声与使用光电循环神经网络的学习算法的相互作用。我们发现噪声在收敛过程中强烈地修改了系统的路径，并且令人惊讶地完全去相关最终读出的权重矩阵。这突出了理解架构的重要性，