Recent results in adaptive matter revived the interest in the implementation of novel devices able to perform brain-like operations. Here we introduce a training algorithm for a memristor network which is inspired in previous work on biological learning. Robust results are obtained from computer simulations of a network of voltage controlled memristive devices. Its implementation in hardware is straightforward, being scalable and requiring very little peripheral computation overhead.

中文翻译：

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忆阻器网络从错误中学习

自适应物质的最新研究结果重新唤起了人们对能够执行类似大脑操作的新型设备的实施的兴趣。在这里，我们介绍了一种忆阻器网络的训练算法，该算法受到之前生物学习工作的启发。可靠的结果是从电压控制的忆阻器件网络的计算机模拟中获得的。它在硬件中的实现很简单，可扩展并且需要很少的外围计算开销。