Publication date: 15th December 2025
Self-organizing memristive networks have been demonstrated as physical substrates for in materia implementation of unconventional computing paradigms such as reservoir computing [1,2]. However, a detailed understanding of the relationship between electrical properties, emergent dynamics and computational properties in these multiterminal systems remain challenging. Here, we report on emergent functionalities in multiterminal nanowire networks through a combined experimental and modeling approach. Beyond extending the traditional two-terminal memristive concept to multiterminal architectures, we discuss experimental characterization techniques used to analyze these emergent behaviors, including conductance matrices, voltage maps, and conductivity maps obtained via electrical resistance tomography. By linking self-organizing memristive systems to dynamical systems theory, we introduce a new framework that enables the description of self-organizing nanowire networks as stochastic dynamical systems [3]. In this framework, emergent dynamics are modeled as an Ornstein-Uhlenbeck process, where stimulus-dependent deterministic trajectories coexist with stochastic effects such as noise and discrete jumps. Within this context, we demonstrate that the emergent dynamics of nanowire-based self-organizing systems can be harnessed for computation. We evaluate their computational capabilities through benchmark tasks, including nonlinear autoregressive moving average (NARMA) and nonlinear transformation (NLT) tasks, and discuss the implementation of pattern recognition, speech recognition, and time-series prediction.
G.M. acknowledge funding by the European Union (ERC, "MEMBRAIN", No. 101160604). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. G.M and C.R. acknowledge funding by NEURONE, a project funded by the European Union - Next Generation EU, M4C1 CUP I53D23003600006, under program PRIN 2022 (prj code 20229JRTZA). EM thanks the support from the Spanish Ministerio de Ciencia e Innovación (MCIN)/Agencia Española de investigación (AEI) 10.13039/501100011 033 (Under project No. PID2022-139586NB-C41) and Subprograma de Movilidad, Plan Estatal de Investigación Científica, Técnica y de Innovación (PEICTI) 2021-2023. Part of this work has been carried out at Nanofacility Piemonte INRiM, a laboratory supported by the ‘‘Compagnia di San Paolo’’ Foundation, and at the QR Laboratories, INRiM.
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