Emergence of winner-takes-all connectivity paths in random nanowire networks

• Published in: Nature communications Vol. 9; no. 1; pp. 3219 - 9
• Main Authors: Manning, Hugh G., Niosi, Fabio, da Rocha, Claudia Gomes, Bellew, Allen T., O’Callaghan, Colin, Biswas, Subhajit, Flowers, Patrick F., Wiley, Benjamin J., Holmes, Justin D., Ferreira, Mauro S., Boland, John J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.08.2018; Nature Publishing Group; Nature Portfolio
• Subjects: 119/118; 639/301/1005/1007; 639/301/357/1016; 639/925/357/995; 639/925/929/115; Biological effects; Coding; Conductance; Connectivity; Emergence; Humanities and Social Sciences; multidisciplinary; Nanotechnology; Nanowires; Networks; Neural networks; Plateaus; Resistance; Scaling; Science; Science (multidisciplinary); Self-similarity
• ISSN: 2041-1723; 2041-1723
• 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. Nanowire networks with memristive properties are promising for neuromorphic applications. Here, the authors observe the formation of a preferred conduction pathway which uses the lowest possible energy to get through the network and could be exploited for the design of optimal brain-inspired devices.