具有反馈电流的自旋电子库中记忆函数的周期结构
Periodic structure of memory function in spintronics reservoir with feedback current
摘要：
通过求解纳米结构铁磁体中的涡核动力学，从理论上研究了反馈效应对物理库计算的作用。虽然由于反馈电流的自旋转移力矩使涡动力学复杂，它是澄清的反馈效应并不总是有助于增强的物理水库中的记忆功能。当反馈电流的延迟时间不是脉冲宽度的整数倍时，记忆函数变大，其特征在于输入数据与涡核动态响应之间的相关系数。另一方面，当延迟时间是脉冲宽度的整数倍时，记忆功能保持较小。其结果是，短期记忆容量的周期性行为被观察到相对于延迟时间，其现象可以归因于虚拟神经元之间的相关性通过反馈电流。
英文摘要：
The role of the feedback effect on physical reservoir computing is studied theoretically by solving the vortex-core dynamics in a nanostructured ferromagnet. Although the spin-transfer torque due to the feedback current makes the vortex dynamics complex, it is clarified that the feedback effect does not always contribute to the enhancement of the memory function in a physical reservoir. The memory function, characterized by the correlation coefficient between the input data and the dynamical response of the vortex core, becomes large when the delay time of the feedback current is not an integral multiple of the pulse width. On the other hand, the memory function remains small when the delay time is an integral multiple of the pulse width. As a result, a periodic behavior for the short-term memory capacity is observed with respect to the delay time, the phenomenon of which can be attributed to correlations between the virtual neurons via the feedback current.
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