论文标题
在Memristor网络中通过错误学习
Learning by mistakes in memristor networks
论文作者
论文摘要
自适应物质的最新结果恢复了对实施能够执行大脑样操作的新型设备的兴趣。在这里，我们介绍了一种用于备忘录网络的培训算法，该算法是在以前的生物学习方面启发的。可靠的结果是从电压控制的磁场设备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，几乎不需要外围计算开销。
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.
