Memristors have attracted interest as neuromorphic computation elements because they show promise in enabling efficient hardware implementations of artificial neurons and synapses. We performed measurements on interface-type memristors to validate their use in neuromorphic hardware. Specifically, we utilised Nb-doped SrTiO$_3$ memristors as synapses in a simulated neural network by arranging them into differential synaptic pairs, with the weight of the connection given by the difference in normalised conductance values between the two paired memristors. This network learned to represent functions through a training process based on a novel supervised learning algorithm, during which discrete voltage pulses were applied to one of the two memristors in each pair. To simulate the fact that both the initial state of the physical memristive devices and the impact of each voltage pulse are unknown we injected noise at each time step. Nevertheless, discrete updates based on local knowledge were shown to result in robust learning performance. Using this class of memristive devices as the synaptic weight element in a spiking neural network yields, to our knowledge, one of the first models of this kind, capable of learning to be a universal function approximator, and strongly suggests the suitability of these memristors for usage in future computing platforms.

中文翻译：

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学习使用 Nb 掺杂的 SrTiO$_3$ 忆阻器逼近函数

忆阻器作为神经形态计算元件引起了人们的兴趣，因为它们在实现人工神经元和突触的高效硬件实现方面显示出前景。我们对接口型忆阻器进行了测量，以验证它们在神经形态硬件中的使用。具体来说，我们通过将 Nb 掺杂的 SrTiO$_3$ 忆阻器排列成差分突触对，将它们用作模拟神经网络中的突触，连接的权重由两个配对忆阻器之间归一化电导值的差异给出。该网络通过基于新型监督学习算法的训练过程学会了表示函数，在此期间将离散电压脉冲施加到每对中的两个忆阻器中的一个。为了模拟物理忆阻器件的初始状态和每个电压脉冲的影响都是未知的事实，我们在每个时间步注入噪声。尽管如此，基于本地知识的离散更新被证明可以产生强大的学习性能。使用这类忆阻器作为尖峰神经网络中的突触权重元素，据我们所知，这是此类最早的模型之一，能够学习成为通用函数逼近器，并强烈表明这些忆阻器适用于在未来的计算平台中使用。