We report a detailed study of neuromorphic switching behaviour in inherently complex percolating networks of self-assembled metal nanoparticles. We show that variation of the strength and duration of the electric field applied to this network of synapse-like atomic switches allows us to control the switching dynamics. Switching is observed for voltages above a well-defined threshold, with higher voltages leading to increased switching rates. We demonstrate two behavioral archetypes and show how the switching dynamics change as a function of duration and amplitude of the voltage stimulus. We show that the state of each synapse can influence the activity of the other synapses, leading to complex switching dynamics. We further demonstrate the influence of the morphology of the network on the measured device properties, and the constraints imposed by the overall network conductance. The correlated switching dynamics, device stability over long periods, and the simplicity of the device fabrication provide an attractive pathway to practical implementation of on-chip neuromorphic computing.

中文翻译：

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复杂的自组装纳米颗粒网络中的突触动力学

我们报告了在自组装金属纳米粒子的内在复杂的渗滤网络中神经形态转换行为的详细研究。我们表明，应用于这种突触状原子开关网络的电场强度和持续时间的变化使我们能够控制开关动力学。在高于明确定义的阈值的电压下观察到开关，较高的电压导致开关速率增加。我们演示了两种行为原型，并显示了开关动力学如何随电压刺激的持续时间和幅度而变化。我们表明每个突触的状态可以影响其他突触的活动，导致复杂的切换动力学。我们进一步证明了网络形态对所测设备性能的影响，以及整个网络电导带来的约束。相关的开关动力学，长期的器件稳定性以及器件制造的简单性为片上神经形态计算的实际实现提供了一条有吸引力的途径。