ABSTRACT

Nanowire networks represent a unique class of neuromorphic systems. Their self-assembly confers a complex structure to their network circuitry, embedding a higher interconnectivity of resistive switching memory (memristive) cross-point junctions than can be achieved with top-down nanofabrication methods. Coupling of the nonlinear memristive dynamics to the network topology enables intrinsic adaptiveness and gives rise to emergent non-local dynamics. In this article, we summarise the physical principles underlying the memristive junctions and network dynamics of neuromorphic nanowire networks and provide the first comprehensive review of studies to date. We conclude with a perspective on future prospects for neuromorphic information processing.

摘要

纳米线网络代表了一类独特的神经形态系统。他们的自组装为其网络电路赋予了复杂的结构，与自上而下的纳米制造方法相比，嵌入了电阻式开关存储器（忆阻）交叉点结的更高互连性。非线性忆阻动力学与网络拓扑的耦合实现了固有的适应性，并产生了新兴的非局部动力学。在本文中，我们总结了基于神经形态的纳米线网络的忆阻性连接和网络动力学的物理原理，并提供了迄今为止有关研究的第一篇综述。我们以对神经形态信息处理的未来前景的观点作为结束。