Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, California 93106, USA.
Nat Commun. 2013;4:2072. doi: 10.1038/ncomms3072.
Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks. Whereas demonstrations of the synaptic operation of memristors already exist, the implementation of even simple networks is more challenging and has yet to be reported. Here we demonstrate pattern classification using a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods. In the first case, synaptic weights, which are realized as conductances of titanium dioxide memristors, are calculated on a precursor software-based network and then imported sequentially into the crossbar circuit. In the second case, training is implemented in situ, so the weights are adjusted in parallel. Both methods work satisfactorily despite significant variations in the switching behaviour of the memristors. These results give hope for the anticipated efficient implementation of artificial neuromorphic networks and pave the way for dense, high-performance information processing systems.
忆阻器是一种记忆电阻器，有望在人工神经网络中高效实现突触权重。虽然已经有忆阻器的突触操作演示，但即使是简单网络的实现也更具挑战性，尚未有报道。在这里，我们展示了使用忆阻器交叉开关电路实现的单层感知器网络进行模式分类，该网络使用感知器学习规则通过原位和异位方法进行训练。在第一种情况下，作为二氧化钛忆阻器电导的突触权重在基于软件的前体网络上进行计算，然后顺序导入交叉开关电路。在第二种情况下，训练是原位进行的，因此权重可以并行调整。尽管忆阻器的开关行为存在显著差异，但这两种方法都能令人满意地工作。这些结果为人工神经形态网络的预期高效实现带来了希望，并为密集、高性能的信息处理系统铺平了道路。
