Neuromorphic computing aims to develop hardware platforms that emulate the effectiveness of our brain. In this context, brain-inspired self-organizing memristive networks have been demonstrated as promising physical substrates for in materia computing. However, understanding the connection between network dynamics and information processing capabilities in these systems still represents a challenge. In this work, we show that neuromorphic nanowire network behavior can be modeled as an Ornstein-Uhlenbeck process which holistically combines stimuli-dependent deterministic trajectories and stochastic effects. This unified modeling framework, able to describe main features of network dynamics including noise and jumps, enables the investigation and quantification of the roles played by deterministic and stochastic dynamics on computing capabilities of the system in the context of physical reservoir computing. These results pave the way for the development of physical computing paradigms exploiting deterministic and stochastic dynamics in the same hardware platform in a similar way to what our brain does.

Self‐organized criticality (SOC) has attracted large interest as a key property for the optimization of information processing in biological neural systems. Inspired by this synergy, nanoscale self‐organizing devices are demonstrated to emulate critical dynamics due to their complex nature, proving to be ideal candidates for the hardware implementation of brain‐inspired unconventional computing paradigms. However, controlling the emerging critical dynamics and understanding its relationship with computing capabilities remains a challenge. Here, it is shown that memristive nanowire networks (NWNs) can be programmed in a critical state through appropriate electrical stimulation. Furthermore, multiterminal electrical characterization reveals that network areas can establish spatial interactions endowing local critical dynamics. The impact of such tunable and local dynamics versus the information processing in the network is experimentally analyzed through in materia implementation of nonlinear transformation (NLT) tasks, in the framework of reservoir computing. As for brain where cortical areas are specialized for a certain function, it is demonstrated that the computing performance of nanowire networks rely on the response of reduced subsets of outputs, which may show critical dynamics or not, depending on the specificity of the task. Such brain‐like behavior can lead to neuromorphic systems based on self‐organizing networks with reduced hardware complexity by exploiting their local and specialized behavior.

Self‐organizing memristive nanowire (NW) networks are promising candidates for neuromorphic‐type data processing in a physical reservoir computing framework because of their collective emergent behavior, which enables spatiotemporal signal processing. However, understanding emergent dynamics in multiterminal networks remains challenging. Here experimental spatiotemporal characterization of memristive NW networks dynamics in multiterminal configuration is reported, analyzing the activation and relaxation of network's global and local conductance, as well as the inherent spatial nonlinear transformation capabilities. Emergent effects are analyzed i) during activation, by investigating the spatiotemporal dynamics of the electric field distribution across the network through voltage mapping; ii) during relaxation, by monitoring the evolution of the conductance matrix of the multiterminal system. The multiterminal approach also allowed monitoring the spatial distribution of nonlinear activity, demonstrating the impact of different network areas on the system's information processing capabilities. Nonlinear transformation tasks are experimentally performed by driving the network into different conductive states, demonstrating the importance of selecting proper operating conditions for efficient information processing. This work allows a better understanding of the local nonlinear dynamics in NW networks and their impact on the information processing capabilities, providing new insights for a rational design of self‐organizing neuromorphic systems.

Copper oxide nanowires (NWs) are promising elements for the realization of a wide range of devices for low-power electronics, gas sensors, and energy storage applications, due to their high aspect ratio, low environmental impact, and cost-effective manufacturing. Here, we report on the electrical and thermal properties of copper oxide NWs synthetized through thermal growth directly on copper foil. Structural characterization revealed that the growth process resulted in the formation of vertically aligned NWs on the Cu growth substrate, while the investigation of chemical composition revealed that the NWs were composed of CuO rather than Cu₂O. The electrical characterization of single-NW-based devices, in which single NWs were contacted by Cu electrodes, revealed that the NWs were characterized by a conductivity of 7.6 × 10⁻² S∙cm⁻¹. The effect of the metal–insulator interface at the NW–electrode contact was analyzed by comparing characterizations in two-terminal and four-terminal configurations. The effective thermal conductivity of single CuO NWs placed on a substrate was measured using Scanning Thermal Microscopy (SThM), providing a value of 2.6 W∙m⁻¹∙K⁻¹, and using a simple Finite Difference model, an estimate for the thermal conductivity of the nanowire itself was obtained as 3.1 W∙m⁻¹∙K⁻¹. By shedding new light on the electrical and thermal properties of single CuO NWs, these results can be exploited for the rational design of a wide range of optoelectronic devices based on NWs.

The recent revision of the International System of Units (SI)—which fixed the numerical values of nature’s fundamental constants—has opened new perspectives for practical realizations of SI units. Here we demonstrate an intrinsic resistance standard based on memristive nanoionic cells that operate in air at room temperature and are directly accessible to end users. By driving these devices into the quantum conductance regime and using an electrochemical-polishing-based programming strategy, we achieved quantum conductance levels that can be exploited as intrinsic standard values. An interlaboratory comparison confirmed metrological consistency, with deviations of –3.8% and 0.6% from the agreed SI values for the fundamental quantum of conductance, G ₀ , and 2 G ₀ , respectively. These results lay the groundwork for the implementation of national metrology institute services on chip and for the development of self-calibrating measurement systems with zero-chain traceability.

Neuromorphic computing aims to develop software and hardware platforms emulating the information processing effectiveness of our brain. In this context, self-organizing neuromorphic nanonetworks have been demonstrated as suitable physical substrates for in materia implementation of unconventional computing paradigms, like reservoir computing. However, understanding the relationship between emergent dynamics and information processing capabilities still represents a challenge. Here, we demonstrate that nanowire-based neuromorphic networks are stochastic dynamical systems where the signals flow relies on the intertwined action of deterministic and random factors. We show through an experimental and modeling approach that these systems combine stimuli-dependent deterministic trajectories and random effects caused by noise and jumps that can be holistically described by an Ornstein-Uhlenbeck process, providing a unifying framework surpassing current modeling approaches of self-organizing neuromorphic nanonetworks (not only nanowire-based) that are limited to either deterministic or stochastic effects. Since information processing capabilities can be dynamically tuned by controlling the network’s attractor memory state, these results open new perspectives for the rational development of physical computing paradigms exploiting deterministic and stochastic dynamics in a single hardware platform similarly to our brain.