Neuromorphic computing aims at the realization of intelligent systems able to process information similarly to our brain. Brain-inspired computing paradigms have been implemented in crossbar arrays of memristive devices; however, this approach does not emulate the topology and the emergent behaviour of biological neuronal circuits, where the principle of self-organization regulates both structure and function. Here, we report on in materia reservoir computing in a fully memristive architecture based on self-organized nanowire networks. Thanks to the functional synaptic connectivity with nonlinear dynamics and fading memory properties, the designless nanowire complex network acts as a network-wide physical reservoir able to map spatio-temporal inputs into a feature space that can be analysed by a memristive resistive switching memory read-out layer. Computing capabilities, including recognition of spatio-temporal patterns and time-series prediction, show that the emergent memristive behaviour of nanowire networks allows in materia implementation of brain-inspired computing paradigms characterized by a reduced training cost.

神经形态计算旨在实现能够像我们的大脑一样处理信息的智能系统。受大脑启发的计算范式已在忆阻设备的交叉阵列中实现；然而，这种方法不能模拟生物神经元电路的拓扑结构和紧急行为，其中自组织原理调节结构和功能。在这里，我们在材料中报道基于自组织纳米线网络的全忆阻架构中的水库计算。由于具有非线性动力学和衰减记忆特性的功能性突触连接，无设计纳米线复杂网络充当网络范围的物理存储库，能够将时空输入映射到特征空间，该特征空间可以通过忆阻电阻切换存储器读取进行分析。出层。计算能力，包括识别时空模式和时间序列预测，表明纳米线网络的新兴忆阻行为允许在材料中实施以降低培训成本为特征的受大脑启发的计算范例。