Self-organizing memristive networks are physical circuits that dynamically reconfigure their circuitry in response to external input signals. Their adaptive behavior arises from intrinsic neuro-synaptic dynamics combined with a heterogeneous network topology. In this work, we demonstrate that such networks naturally generate neuronal population spiking dynamics similar to those observed in biological neuronal systems. This study investigates the intrinsic and emergent dynamics of memristive networks mathematically and numerically for both DC and AC input signals. Nonlinear spike-like features are maximized when the frequency of the input driving signal matches the network's intrinsic dynamical timescale, where nonlinear resonance is observed. Furthermore, the optimal frequency for computation is found to be the maximal frequency before the onset of resonance.

The rapid growth of artificial intelligence (AI) is increasingly constrained by fundamental hardware bottlenecks in computation throughput and energy efficiency. Bioinspired computing (BIC) offers a promising alternative by emulating the intrinsic advantages of biological systems, such as parallelism, adaptability, and robustness. Progress in BIC hardware demands interdisciplinary convergence that bridges materials science and device physics with neuroscience, computer science, mathematics, and information science. Therefore, the development of this cross-disciplinary field urgently requires a comprehensive roadmap that analyzes systematically and in-depth the frontier issues and the latest progress. In this roadmap, we categorize the critical challenges into three components: hardware foundations, architectures, and prototype realizations. We highlight how biological features inspire the design of BIC hardware through device physics and discuss their performance metrics and engineering challenges. We then describe how diverse signaling rules and structural organizations in BIC architectures support specific computational prototypes, including electronic and photonic BIC chips, and present a technological roadmap that outlines opportunities to expand the functional scope of BIC hardware through coordinated advances in devices, architectures, and system demonstrations. This ongoing convergence of interdisciplinary knowledge can help accelerate the shift toward high-efficiency AI hardware.

In Positron Emission Tomography, a potential, yet unutilized enhancement, may come from exploiting the quantum entanglement of the annihilation quanta, inscribed in the correlation of their polarizations. To investigate this, we built a PET demonstrator capable of measuring polarization correlations of annihilation quanta by their Compton scattering, based on single-layer scintillator polarimeters. We present a detailed study of the imaging of two ^68Ge line sources, 45 MBq each, to extract the spatial resolution and assess image quality. The results show that a spatial resolution of 2.5±0.1 mm is obtained using single-pixel events, while resolutions obtained with polarization-correlated Compton events range from 3.6±0.3 mm to 4.9±0.3 mm, depending on data selection criteria. We also found that the polarization-correlated Compton events exhibit up to 20

Annihilation photons are quantum entangled in their polarization, a property that is not accessible in state-of-the-art clinical Positron Emission Tomography (PET). This roadmap describes the current status of research in the emerging field of quantum entanglement applications involving these photons for medical diagnosis. It outlines the underlying physics phenomena and the development of detector systems that can serve as a foundation for future Quantum Entanglement PET (QE-PET) scanners. These scanners will be capable of utilizing the entanglement between annihilation photons by measuring their Compton scattering on electrons. This roadmap comprises up-to-date experimental results on the study of quantum entanglement and the decoherence of annihilation photons, alongside the current theoretical understanding of these phenomena. The methods and detector technologies described herein are being developed (i) in order to enhance standard PET imaging by suppressing random coincidences in the reconstruction of annihilation site density distributions, (ii) in order to establish the degree of quantum entanglement as a diagnostic biomarker for tissue pathology and oxygenation, and (iii) in order to elaborate a method for pH imaging. Whether entanglement-based imaging and pH mapping can be successfully translated into clinical practice remains an open question and a subject of exciting ongoing research. This roadmap serves as an invitation to the scientific community to join this burgeoning field.

Damage caused by bushfires and volcanic eruptions escalates rapidly when detection is delayed, making fast and reliable early warning capabilities essential. Recent Earth Observation (EO) approaches have shown that thermal anomaly detection can be performed directly on decompressed Level-0 (L0) sensor data, avoiding computationally expensive preprocessing chains. However, direct exploitation of raw data remains challenging due to domain shift, sensor drift, radiometric inconsistencies, and the scarcity of labelled training samples. To address these challenges, this work proposes a Physics-Aware Neuromorphic Network (PANN) framework for onboard thermal anomaly detection. The proposed lightweight architecture, inspired by physical neural network principles and neuromorphic computing paradigms, is evaluated using two Sentinel-2 datasets: decompressed L0 with additional metadata (i.e. raw) and Level-1C (L1C). The PANN achieves a Matthews Correlation Coefficient (MCC) of 0.809 on raw measurements, compared to 0.875 when using ground-processed L1C products. The mean processing latency per L0 granule is 2.44 ± 0.09 s, which is below the Sentinel-2 acquisition time of 3.6 s, demonstrating the feasibility of real-time, onboard processing. Furthermore, the projected execution time for the corresponding neuromorphic hardware instantiation is substantially lower at 0.1290 ± 0.0002 s. Memory usage, including all necessary programs and packages, remains within realistic onboard constraints, with requirements of 0.673 ± 0.007 Gb for the software PANN and 0.393 ± 0.004 Gb for the estimated hardware realisation. Overall, these results indicate that PANN offers a promising pathway toward low-latency and resource-efficient onboard EO processing for thermal event detection.

Reservoir Computing (RC) with physical systems requires an understanding of the underlying structure and internal dynamics of the specific physical reservoir. In this study, physical nano-electronic networks with neuromorphic dynamics are investigated for their use as physical reservoirs in an RC framework. These neuromorphic networks operate as dynamic reservoirs, with node activities in general coupled to the edge dynamics through nonlinear nano-electronic circuit elements, and the reservoir outputs influenced by the underlying network connectivity structure. This study finds that networks with varying degrees of sparsity generate more useful nonlinear temporal outputs for dynamic RC compared to dense networks. Dynamic RC is also tested on an autonomous multivariate chaotic time series prediction task with networks of varying densities, which revealed the importance of network sparsity in maintaining network activity and overall dynamics, that in turn enabled the learning of the chaotic Lorenz63 system's attractor behavior.

Earth observations from low Earth orbit satellites provide vital information for decision makers to better manage time-sensitive events such as natural disasters. For the data to be most effective for first responders, low latency is required between data capture and its arrival to decision makers. A major bottleneck is in the bandwidth-limited downlinking of the data from satellites to ground stations. One approach to overcome this challenge is to process at least some of the data on-board and prioritise pertinent data to be downlinked. In this study we propose a Physics Aware Neuromorphic Network (PANN) to detect changes caused by natural disasters from a sequence of multi-spectral satellite images and produce a change map, enabling relevant data to be prioritised for downlinking. The PANN used in this study is motivated by physical neural networks comprised of nano-electronic circuit elements known as “memristors” (nonlinear resistors with memory). The weights in the network are dynamic and update in response to varying input signals according to memristor equations of state and electrical circuit conservation laws. The PANN thus generates physics-constrained dynamical output features which are used to detect changes in a natural disaster detection task by applying a distance-based metric. Importantly, this makes the whole model training-free, allowing it to be implemented with minimal computing resources. The PANN was benchmarked against a state-of-the-art AI model and achieved comparable or better results in each natural disaster category. It thus presents a promising solution to the challenge of resource-constrained on-board processing.

Detecting glioblastoma infiltration in the brain is challenging due to limited MRI contrast beyond the enhancing tumour core. This study aims to investigate the potential of superparamagnetic iron oxide nanoparticles (SPIONs) as contrast agents for improved detection of diffuse brain cancer. We examine the distribution and pharmacokinetics of SPIONs in glioblastoma models with intact and disrupted blood-brain barriers. Using MRI, we imaged RN1-luc and U87MG mice injected with Gadovist and SPIONs, observing differences in blood-brain barrier permeability. Peripheral imaging showed strong uptake of nanoparticles in the liver and spleen, while vascular and renal signals were transient. Susceptibility gradient mapping enabled positive nanoparticle contrast within tumours and provided additional information on tumour angiogenesis. This approach offers a novel method for detecting diffuse brain cancer. Our findings demonstrate that SPIONs enhance glioblastoma detection beyond conventional MRI, providing insights into tumour angiogenesis and opening new avenues for early diagnosis and targeted treatment strategies.

Learning with physical systems is an emerging paradigm that seeks to harness the intrinsic nonlinear dynamics of physical substrates for learning. The impetus for a paradigm shift in how hardware is used for computational intelligence stems largely from the unsustainability of artificial neural network software implemented on conventional transistor-based hardware. This Perspective highlights one promising approach using physical networks comprised of resistive memory nanoscale components with dynamically reconfigurable, self-organising electrical circuitry. Experimental advances have revealed the non-trivial interactions within these Self-Organising Memristive Networks (SOMNs), offering insights into their collective nonlinear and adaptive dynamics, and how these properties can be harnessed for learning using different hardware implementations. Theoretical approaches, including mean-field theory, graph theory, and concepts from disordered systems, reveal deeper insights into the dynamics of SOMNs, especially during transitions between different conductance states where criticality and other dynamical phase transitions emerge in both experiments and models. Furthermore, parallels between adaptive dynamics in SOMNs and plasticity in biological neuronal networks suggest the potential for realising energy-efficient, brain-like continual learning. SOMNs thus offer a promising route toward embedded edge intelligence, unlocking real-time decision-making for autonomous systems, dynamic sensing, and personalised healthcare, by enabling embedded learning in resource-constrained environments. The overarching aim of this Perspective is to show how the convergence of nanotechnology, statistical physics, complex systems, and self-organising principles offers a unique opportunity to advance a new generation of physical intelligence technologies.

This study investigates how dynamical systems may be learned and modeled with a neuromorphic network, which is itself a dynamical system. The neuromorphic network used in this study is based on a complex electrical circuit comprised of memristive elements that produce neuro-synaptic nonlinear responses to input electrical signals. To determine how computation may be performed using the physics of the underlying system, the neuromorphic network was simulated and evaluated on the autonomous prediction of a multivariate chaotic time series, implemented with a reservoir computing framework. Through manipulating only input electrodes and voltages, optimal nonlinear dynamical responses were found when input voltages maximize the number of memristive components whose internal dynamics explore the entire dynamical range of the memristor model. Increasing the network coverage with the input electrodes was found to suppress other nonlinear responses that are less conducive to learning. These results provide valuable insights into how a physical neuromorphic network device can be feasibly optimized for learning complex dynamical systems using only external control parameters.

Networks composed of nanoscale memristive components, such as nanowire and nanoparticle networks, have recently received considerable attention because of their potential use as neuromorphic devices. In this study, we explore ergodicity in memristive networks, showing that the performance on machine leaning tasks improves when these networks are tuned to operate at the edge between two global stability points. We find this lack of ergodicity is associated with the emergence of memory in the system. We measure the level of ergodicity using the Thirumalai-Mountain metric, and we show that in the absence of ergodicity, two different memristive network systems show improved performance when utilized as reservoir computers (RC). We highlight that it is also important to let the system synchronize to the input signal in order for the performance of the RC to exhibit improvements over the baseline.

Abstract Major efforts to reproduce functionalities and energy efficiency of the brain have been focused on the development of artificial neuromorphic systems based on crossbar arrays of memristive devices fabricated by top-down lithographic technologies. Although very powerful, this approach does not emulate the topology and the emergent behavior of biological neuronal circuits, where the principle of self-organization regulates both structure and function. In materia computing has been proposed as an alternative exploiting the complexity and collective phenomena originating from various classes of physical substrates composed of a large number of non-linear nanoscale junctions. Systems obtained by the self-assembling of nano-objects like nanoparticles and nanowires show spatio-temporal correlations in their electrical activity and functional synaptic connectivity with nonlinear dynamics. The development of design-less networks offers powerful brain-inspired computing capabilities and the possibility of investigating critical dynamics in complex adaptive systems. Here we review and discuss the relevant aspects concerning the fabrication, characterization, modeling, and implementation of networks of nanostructures for data processing and computing applications. Different nanoscale electrical conduction mechanisms and their influence on the meso- and macroscopic functional properties of the systems are considered. Criticality, avalanche effects, edge-of-chaos, emergent behavior, synaptic functionalities are discussed in detail together with applications for unconventional computing. Finally, we discuss the challenges related to the integration of nanostructured networks and with standard microelectronics architectures.

Recent studies of positron emission tomography (PET) devices have shown that the detection of polarization-correlated annihilation quanta can potentially reduce the background and creation of false lines of response (LORs) leading to improved image quality. We developed a novel PET demonstrator system, capable of measuring correlated gamma photons with single-layer Compton polarimeters to explore the potential of the method. We tested the system using sources with clinically relevant activities at the University Hospital Centre Zagreb. Here we present, for the first time, the images of two Ge-68 line sources, reconstructed solely from the correlated annihilation events. The spatial resolution at two different diameters is determined and compared to the one obtained from events with photoelectric interaction.

In positron annihilation, exploration of the polarization correlations of the emerging gamma quanta has gained interest, since they offer a possibility to improve signal-to-background in medical imaging using positron emission tomography. The annihilation quanta, which are predicted to be in an entangled state, have orthogonal polarizations and this property may be exploited to discriminate them from two uncorrelated gamma photons contributing to the background. Recent experimental studies of polarization correlations of the annihilation quanta after a prior Compton scattering of one of them, had rather different conclusions regarding the strength of the correlation after the scattering, showing its puzzling nature. The scattering was described as a decoherence process. In the present work, we perform for the first time, a study of the polarization correlations of annihilation quanta after decoherence via Compton scattering in the angular range 0∘−50∘ using single-layer gamma ray polarimeters. In addition, we compare the measured polarization correlations after Compton scattering at 30∘ with an active and a passive scatterer element. The measured azimuthal correlation of back-to-back annihilation quanta is consistent with the Pryce-Ward formulation, as confirmed by Monte Carlo simulations. Further, the results indicate that the correlation, expressed in terms of the polarimetric modulation factor, shows no significant difference at small scattering angles (0∘−30∘) compared to the correlation measured for direct photons, while a moderate indication of a lower modulation is observed for 50∘ scattering angle. The measured modulation is larger at all scattering angles than the one expected from the simulation of orthogonally polarized, independent annihilation quanta.

Nanowire networks (NWNs) mimic the brain’s neurosynaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate the synaptic processes that enable higher-order cognitive functions such as learning and memory. A quintessential cognitive task used to measure human working memory is the n -back task. In this study, task variations inspired by the n -back task are implemented in a NWN device, and external feedback is applied to emulate brain-like supervised and reinforcement learning. NWNs are found to retain information in working memory to at least n = 7 steps back, remarkably similar to the originally proposed “seven plus or minus two” rule for human subjects. Simulations elucidate how synapse-like NWN junction plasticity depends on previous synaptic modifications, analogous to “synaptic metaplasticity” in the brain, and how memory is consolidated via strengthening and pruning of synaptic conductance pathways.

Memristive reservoirs draw inspiration from a novel class of neuromorphic hardware known as nanowire networks. These systems display emergent brain-like dynamics, with optimal performance demonstrated at dynamical phase transitions. In these networks, a limited number of electrodes are available to modulate system dynamics, in contrast to the global controllability offered by neuromorphic hardware through random access memories. We demonstrate that the learn-to-learn framework can effectively address this challenge in the context of optimization. Using the framework, we successfully identify the optimal hyperparameters for the reservoir. This finding aligns with previous research, which suggests that the optimal performance of a memristive reservoir occurs at the 'edge of formation' of a conductive pathway. Furthermore, our results show that these systems can mimic membrane potential behavior observed in spiking neurons, and may serve as an interface between spike-based and continuous processes.

Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switching in response to electrical inputs due to synapse-like changes in conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how the neuromorphic dynamics generated by NWNs can be harnessed for temporal learning tasks. This study extends these findings further by demonstrating online learning from spatiotemporal dynamical features using image classification and sequence memory recall tasks implemented on an NWN device. Applied to the MNIST handwritten digit classification task, online dynamical learning with the NWN device achieves an overall accuracy of 93.4%. Additionally, we find a correlation between the classification accuracy of individual digit classes and mutual information. The sequence memory task reveals how memory patterns embedded in the dynamical features enable online learning and recall of a spatiotemporal sequence pattern. Overall, these results provide proof-of-concept of online learning from spatiotemporal dynamics using NWNs and further elucidate how memory can enhance learning.

Biological neuronal networks are characterized by nonlinear interactions and complex connectivity. Given the growing impetus to build neuromorphic computers, understanding physical devices that exhibit structures and functionalities similar to biological neural networks is an important step toward this goal. Self-organizing circuits of nanodevices are at the forefront of the research in neuromorphic computing, as their behavior mimics synaptic plasticity features of biological neuronal circuits. However, an effective theory to describe their behavior is lacking. This study provides for the first time an effective mean field theory for the emergent voltage-induced polymorphism of \textit{circuits} of a nanowire connectome, showing that the behavior of these circuits can be explained by a low-dimensional dynamical equation. The equation can be derived from the microscopic dynamics of a single memristive junction in analytical form. We test our effective model on experiments of nanowire networks and show that it fits both the potentiation and depression of these synapse-mimicking circuits. We show that our theory applies beyond the case of nanowire networks by formulating a general mean-field theory of conductance transitions in self-organizing memristive connectomes.