School of Chemistry, Trinity College Dublin, Dublin 2, Ireland.
Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN) & Advanced Materials and Bioengineering Research (AMBER) Centre, Trinity College Dublin, Dublin 2, Ireland.
Nat Commun. 2018 Aug 13;9(1):3219. doi: 10.1038/s41467-018-05517-6.
Nanowire networks are promising memristive architectures for neuromorphic applications due to their connectivity and neurosynaptic-like behaviours. Here, we demonstrate a self-similar scaling of the conductance of networks and the junctions that comprise them. We show this behavior is an emergent property of any junction-dominated network. A particular class of junctions naturally leads to the emergence of conductance plateaus and a "winner-takes-all" conducting path that spans the entire network, and which we show corresponds to the lowest-energy connectivity path. The memory stored in the conductance state is distributed across the network but encoded in specific connectivity pathways, similar to that found in biological systems. These results are expected to have important implications for development of neuromorphic devices based on reservoir computing.
纳米线网络由于其连接性和类神经突触行为，是有前途的神经形态应用的忆阻架构。在这里，我们展示了网络及其组成结的电导的自相似标度。我们表明，这种行为是任何由结主导的网络的一个涌现特性。一类特殊的结自然导致电导平台的出现和跨越整个网络的“赢家通吃”的导电路径，我们表明这对应于最低能量连接路径。存储在电导状态中的记忆分布在整个网络中，但编码在特定的连接路径中，类似于在生物系统中发现的记忆。这些结果有望对基于储层计算的神经形态器件的发展产生重要影响。
