An energy-efficient adiabatic learning neuro cell is proposed. The cell can be used for on-chip learning of adiabatic superconducting artificial neural networks. The static and dynamic characteristics of the proposed learning cell have been investigated. Optimization of the learning cell parameters was performed within simulations of the multi-layer neural network supervised learning with the resilient propagation method.

中文翻译：

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用于超导神经网络的学习单元

提出了一种节能的绝热学习神经细胞。该细胞可用于绝热超导人工神经网络的芯片学习。已经研究了所提出的学习单元的静态和动态特性。通过弹性传播方法在多层神经网络监督学习的模拟中执行了学习单元参数的优化。