The emergence of nano-scale memristive devices encouraged many different research areas to exploit their use in multiple applications. One of the proposed applications was to implement synaptic connections in bio-inspired neuromorphic systems. Large-scale neuromorphic hardware platforms are being developed with increasing number of neurons and synapses, having a critical bottleneck in the online learning capabilities. Spike-timing-dependent plasticity (STDP) is a widely used learning mechanism inspired by biology which updates the synaptic weight as a function of the temporal correlation between pre- and post-synaptic spikes. In this work, we demonstrate experimentally that binary stochastic STDP learning can be obtained from a memristor when the appropriate pulses are applied at both sides of the device.

中文翻译：

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基于硫族化物的忆阻器件实现二进制随机STDP学习

纳米级忆阻器件的出现鼓励了许多不同的研究领域来利用它们在多种应用中的用途。提出的应用之一是在生物启发的神经形态系统中实现突触连接。正在开发具有越来越多的神经元和突触的大型神经形态硬件平台，这些平台在在线学习功能中具有严重的瓶颈。尖峰时间依赖性可塑性（STDP）是受到生物学启发的一种广泛使用的学习机制，该机制根据突触前和突触后峰值之间的时间相关性来更新突触权重。在这项工作中，我们通过实验证明，当在器件的两侧都施加适当的脉冲时，可以从忆阻器获得二进制随机STDP学习。