作为储备计算节点的微环：记忆/非线性任务与输入非理想性的影响
A Microring as a Reservoir Computing Node: Memory/Nonlinear Tasks and Effect of Input Non-ideality
AI总结：
本研究将光学微谐振腔用于时分复用储备计算，结合虚拟节点法与岭回归训练实现线性及非线性逻辑运算，验证其最高2比特记忆能力，并指出需将储备池性能与输入信号同训练结果对比。
摘要：
光学微谐振腔的非线性响应被应用于时分复用储备计算神经网络中。结合虚拟节点方法与通过岭回归的离线训练，我们求解了线性与非线性逻辑运算。我们分析了微谐振腔的非线性特性，其可作为比特间的记忆和/或神经激活函数。这一分析通过控制参与逻辑运算的比特间距以及输入岭回归的比特数量得以实现。研究表明，该光学微谐振腔在线性任务中具备最高2比特的记忆能力，且能够同时提供记忆与非线性特性以求解非线性任务。最后，我们证明虚拟节点方法始终需要将储备池的性能与在输入信号上应用相同训练流程得到的结果进行对比。
英文摘要：
The nonlinear response of an optical microresonator is used in a time multiplexed reservoir computing neural network. Within a virtual node approach combined with an offline training through ridge regression, we solved linear and nonlinear logic operations. We analyzed the nonlinearity of the microresonator as a memory between bits and/or as a neural activation function. This is made possible by controlling both the distance between bits subject to the logical operation and the number of bits supplied to the ridge regression. We show that the optical microresonator exhibits up to two bits of memory in linear tasks and that it allows solving nonlinear tasks providing both memory and nonlinearity. Finally, we demonstrate that the virtual node approach always requires a comparison of the reservoir's performance with the results obtained by applying the same training process on the input signal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
