Grid-graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks

Abstract Self-assembled memristive nanonetworks composed of many interacting nano objects have been recently exploited for neuromorphic-type data processing and the implementation unconventional computing paradigms, such as reservoir computing. In these networks, information tasks are performed by exploiting emergent network behaviour without need fine tuning its components. Here, we propose grid-graph modelling nanonetworks, where is decoupled from particular detailed each element. this model, behavior edge regulated an analytical potentiation-depression rate balance equation deduced physical arguments. By comparing experimental results obtained on based Ag NWs, model shown to be able emulate main features spatio-temporal dynamics nanonetwork, including short-term plasticity, paired-pulse facilitation heterosynaptic plasticity. These show that represents a versatile platform exploring paradigms in nanonetworks.