Search Results (56)

Threshold-switching memristors (TSMs) are emerging as key enablers for hardware spiking neural networks, offering intrinsic spiking dynamics, sub-pJ energy consumption, and nanoscale footprints ideal for brain-inspired computing at the edge. This review provides a comprehensive examination of how TSMs emulate diverse spiking behaviors—including oscillatory, leaky integrate-and-fire (LIF), Hodgkin–Huxley (H-H), and stochastic dynamics—and how these features enable compact, energy-efficient neuromorphic systems. We analyze the physical switching mechanisms of redox and Mott-type TSMs, discuss their voltage-dependent dynamics, and assess their suitability for spike generation. We review memristor-based neuron circuits regarding architectures, materials, and key performance metrics. At the system level, we summarize bio-inspired neuromorphic platforms integrating TSM neurons with visual, tactile, thermal, and olfactory sensors, achieving real-time edge computation with high accuracy and low power. Finally, we critically examine key challenges—such as stochastic switching origins, device variability, and endurance limits—and propose future directions toward reconfigurable, robust, and scalable memristive neuromorphic architectures. Full article

Continuous-time Recurrent Neural Networks (CTRNNs) are well-suited for modeling temporal dynamics in low-power neuromorphic and analog computing systems, making them promising candidates for edge-based human activity recognition (HAR) in healthcare. However, training CTRNNs remains challenging due to their continuous-time nature and the need to respect physical hardware constraints. In this work, we propose G-CTRNN, a novel gradient-based training framework for analog-friendly CTRNNs designed for embedded healthcare applications. Our method extends Backpropagation Through Time (BPTT) to continuous domains using TensorFlow’s automatic differentiation, while enforcing constraints on time constants and synaptic weights to ensure hardware compatibility. We validate G-CTRNN on the WISDM human activity dataset, which simulates realistic wearable sensor data for healthcare monitoring. Compared to conventional RNNs, G-CTRNN achieves superior classification accuracy with fewer parameters and greater stability—enabling continuous, real-time HAR on low-power platforms such as MEMS computing networks. The proposed framework provides a pathway toward on-device AI for remote patient monitoring, elderly care, and personalized healthcare in resource-constrained environments. Full article

In recent decades, access to powerful computational resources has brought about a major transformation in science, with supercomputers drawing significant attention from academia, industry, and governments. Among these resources, high-performance computing (HPC) has emerged as one of the most critical processing infrastructures, providing a suitable platform for evaluating and implementing novel technologies. In this context, the development of emerging computing technologies has opened up new horizons in information processing and the delivery of computing services. In this regard, this paper systematically reviews and classifies emerging HPC-related computing technologies, including quantum computing, nanocomputing, in-memory architectures, neuromorphic systems, serverless paradigms, adiabatic technology, and biological solutions. Within the scope of this research, 142 studies which were mostly published between 2018 and 2025 are analyzed, and relevant hardware solutions, domain-specific programming languages, frameworks, development tools, and simulation platforms are examined. The primary objective of this study is to identify the software and hardware dimensions of these technologies and analyze their roles in improving the performance, scalability, and efficiency of HPC systems. To this end, in addition to a literature review, statistical analysis methods are employed to assess the practical applicability and impact of these technologies across various domains, including scientific simulation, artificial intelligence, big data analytics, and cloud computing. The findings of this study indicate that emerging HPC-related computing technologies can serve as complements or alternatives to classical computing architectures, driving substantial transformations in the design, implementation, and operation of high-performance computing infrastructures. This article concludes by identifying existing challenges and future research directions in this rapidly evolving field. Full article

This study reports a gate-tunable three-terminal optoelectronic synaptic device based on an Al/ZnO nanoparticles (NPs)/SiO₂/Si structure for neuromorphic in-sensor memory applications. The ZnO NP film, fabricated via spin coating, exhibited strong UV-induced excitatory post-synaptic current (EPSC) responses that were modulated by gate voltage through charge injection across the SiO₂ dielectric rather than by conventional field effect. Optical stimulation enabled short-term synaptic plasticity, with paired-pulse facilitation (PPF) values reaching 185% at a gate voltage of −5.0 V and decreasing to 180% at +5.0 V, confirming gate-dependent modulation of synaptic weight. Repeated stimulation enhanced learning efficiency and memory retention, as demonstrated by reduced pulse numbers for relearning and slower EPSC decay. Wickelgren’s power law analysis further revealed a decrease in the forgetting rate under negative gate bias, indicating improved long-term memory characteristics. A 3 × 3 synaptic device array visualized visual memory formation through EPSC-based color mapping, with darker intensities and slower fading observed under −5.0 V bias. These results highlight the critical role of gate-voltage-induced charge injection through the SiO₂ dielectric in controlling optical potentiation and electrical depression, establishing ZnO NP-based optoelectronic synaptic devices as promising platforms for energy-efficient, light-driven neuromorphic computing. Full article

Artificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders. However, traditional artificial neural networks (ANNs) face challenges in processing spatiotemporal neuroimaging data due to their limited temporal memory and high computational demands. Spiking neural networks (SNNs), inspired by the brain’s biological processes, offer a promising alternative. SNNs use discrete spikes for event-driven communication, making them energy-efficient and well suited for the real-time processing of dynamic brain data. Among SNN architectures, NeuCube stands out as a powerful framework for analysing spatiotemporal neuroimaging data. It employs a 3D brain-like structure to model neural activity, enabling personalised modelling, disease classification, and biomarker discovery. This paper explores the advantages of SNNs and NeuCube for multimodal neuroimaging analysis, including their ability to handle complex spatiotemporal patterns, adapt to evolving data, and provide interpretable insights. We discuss applications in disease diagnosis, brain–computer interfaces, and predictive modelling, as well as challenges such as training complexity, data encoding, and hardware limitations. Finally, we highlight future directions, including hybrid ANN-SNN models, neuromorphic hardware, and personalised medicine. Our contributions in this work are as follows: (i) we give a comprehensive review of an SNN applied to neuroimaging analysis; (ii) we present current software and hardware platforms, which have been studied in neuroscience; (iii) we provide a detailed comparison of performance and timing of SNN software simulators with a curated ADNI and other datasets; (iv) we provide a roadmap to select a hardware/software platform based on specific cases; and (v) finally, we highlight a project where NeuCube has been successfully used in neuroscience. The paper concludes with discussions of challenges and future perspectives. Full article

In the era of artificial intelligence, the demand for rapid and efficient data processing is growing, and traditional computing architectures are increasingly struggling to meet these needs. Against this backdrop, memristor devices, capable of mimicking the computational functions of brain neural networks, have emerged as key components in neuromorphic systems. Despite this, memristors still face many challenges in biomimetic functionality and circuit integration. In this context, a starch–glycerol-based hydrogel memristor was developed using starch as the dielectric material. The starch–glycerol–water mixture employed in this study has been widely recognized in literature as a physically cross-linked hydrogel system with a three-dimensional network, and both high water content and mechanical flexibility. This memristor demonstrates a high current switching ratio and stable threshold voltage, showing great potential in mimicking the activity of biological neurons. The device possesses the functionality of auditory neurons, not only achieving artificial spiking neuron discharge but also accomplishing the spatiotemporal summation of input information. In addition, we demonstrate the application capabilities of this artificial auditory neuron in gain modulation and in the synchronization detection of sound signals, further highlighting its potential in neuromorphic engineering applications. These results suggest that starch-based hydrogel memristors offer a promising platform for the construction of bio-inspired auditory neuron circuits and flexible neuromorphic systems. Full article

Nonvolatile switching is emerging and shows potential in integrated optics. A compact nonvolatile reconfigurable mode converter implemented on a 4H-silicon-carbide-on-insulator (4H-SiCOI) platform with a footprint of 0.5 × 1 × 1.8 μm³ is proposed in this study. The functional region features an Sb₂S₃ film embedded in a 4H-SiC strip waveguide. The functionality is achieved through manipulating the phase state of the Sb₂S₃. The high refractive index contrast between the crystalline Sb₂S₃ and 4H-SiC enables high-efficiency mode conversion within a compact footprint. The incident TM0 mode is converted to the TM1 mode with a high transmittance (T) beyond 0.91 and a mode purity (MP) over 91.72% across the 1500–1600 nm waveband. Additionally, when the Sb₂S₃ transitions to its amorphous state, the diminished refractive index contrast efficiently mitigates the mode conversion effect. In this state, the TM0 mode propagates through the functional region with minimal perturbation, exhibiting T ≥ 0.99 and MP_TM0 ≥ 97.65% within a 1500–1600 nm waveband. Furthermore, the device performances were investigated under partially crystallized states of Sb₂S₃. The proposed structure offers a broad range of transmittance differences (−16.42 dB ≤ ΔT ≤ 17.1 dB) and mode purity differences (−90.91% ≤ ΔMP ≤ 96.11%) between the TM0 mode and TM1 mode. The proposed device exhibits a high robustness within ±20 nm Δl and ±10 nm Δw. We believe that the proposed multi-level manipulation can facilitate a large communication capacity and that it can be deployed in neuromorphic optical computing. Full article

We report an ion-gel-gated amorphous indium gallium zinc oxide (a-IGZO) optoelectronic neuromorphic transistors capable of synaptic emulation in both photoelectric dual modes. The ion-gel dielectric in the coplanar-structured transistor, fabricated via ink-jet printing, exhibits excellent double-layer capacitance (>1 μF/cm²) and supports low-voltage operation through lateral gate coupling. The integration of ink-jet printing technology enables scalable and large-area fabrication, highlighting its industrial feasibility. Electrical stimulation-induced artificial synaptic behaviors were successfully demonstrated through ion migration in the gel matrix. Through a simple and controllable oxygen vacancy engineering process involving low-temperature oxygen-free growth and post-annealing process, a sufficient density of stable subgap states was generated in IGZO, extending its responsivity spectrum to the visible-red region and enabling wavelength-discriminative photoresponses to 450/532/638 nm visible light. Notably, the subgap states exhibited unique interaction dynamics with low-energy photons in optically triggered pulse responses. Critical synaptic functionalities—including short-term plasticity (STP), long-term plasticity (LTP), and paired-pulse facilitation (PPF)—were successfully simulated under both optical and electrical stimulations. The device achieves low energy consumption while maintaining compatibility with flexible substrates through low-temperature processing (≤150 °C). This study establishes a scalable platform for multimodal neuromorphic systems utilizing printed iontronic architectures. Full article

The development of cybernetic organisms—autonomous systems capable of self-regulation and dynamic environmental interaction—requires innovations in both energy efficiency and computational adaptability. This study explores the integration of bio-inspired liquid flow batteries and neuromorphic computing architectures to enable real-time learning and power optimization in autonomous robotic systems. Liquid-based energy storage systems, modeled after vascular networks, offer distributed energy management, reducing power bottlenecks and improving resilience in long-duration operations. Complementing this, neuromorphic computing architectures, including memristor-based processors and spiking neural networks (SNNs), enhance computational efficiency while minimizing energy consumption. By integrating these adaptive energy and computing systems, robots can dynamically allocate power and processing resources based on real-time demands, bridging the gap between biological and artificial intelligence. This study evaluates the feasibility of integrating these technologies into robotic platforms, assessing power demands, storage capacity, and operational scalability. While flow batteries and neuromorphic computing show promise in reducing latency and energy constraints, challenges remain in electrolyte stability, computational framework standardization, and real-world implementation. Future research must focus on hybrid computing architectures, self-regulating energy distribution, and material optimizations to enhance the adaptability of cybernetic organisms. By addressing these challenges, this study outlines a roadmap for reimagining robotics through cybernetic principles, paving the way for applications in healthcare, industrial automation, space exploration, and adaptive autonomous systems in dynamic environments. Full article

Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a novel neuromorphic framework of a BMI system for prosthetics control via decoding Electro Cortico-Graphic (ECoG) brain signals. It includes a three-dimensional spike timing neural network (3D-SNN) for brain signals features extraction and an on-line trainable recurrent reservoir structure (Echo state network (ESN)) for Motor Control Decoding (MCD). A software system, written in Python using NEST Simulator SNN library is described. It is able to adapt continuously in real time in supervised or unsupervised mode. The proposed approach was tested on several experimental data sets acquired from a tetraplegic person. First simulation results are encouraging, showing also the need for a further improvement via multiple hyper-parameters tuning. Its future implementation on a neuromorphic hardware platform that is smaller in size and significantly less power consuming is discussed too. Full article

In this paper, we propose an optimization approach using Particle Swarm Optimization (PSO) to enhance reservoir separability in Liquid State Machines (LSMs) for spatio-temporal classification in neuromorphic systems. By leveraging PSO, our method fine-tunes reservoir parameters, neuron dynamics, and connectivity patterns, maximizing separability while aligning with the resource constraints typical of neuromorphic hardware. This approach was validated in both software (NEST) and on neuromorphic hardware (SpiNNaker), demonstrating notable results in terms of accuracy and low energy consumption when using SpiNNaker. Specifically, our approach addresses two problems: Frequency Recognition (FR) with five classes and Pattern Recognition (PR) with four, eight, and twelve classes. For instance, in the Mono-objective approach running in NEST, accuracies ranged from 81.09% to 95.52% across the benchmarks under study. The Multi-objective approach outperformed the Mono-objective approach, delivering accuracies ranging from 90.23% to 98.77%, demonstrating its superior scalability for LSM implementations. On the SpiNNaker platform, the mono-objective approach achieved accuracies ranging from 86.20% to 97.70% across the same benchmarks, with the Multi-objective approach further improving accuracies, ranging from 94.42% to 99.52%. These results show that, in addition to slight accuracy improvements, hardware-based implementations offer superior energy efficiency with a lower execution time. For example, SpiNNaker operates at around 1–5 watts per chip, while traditional systems can require 50–100 watts for similar tasks, highlighting the significant energy savings of neuromorphic hardware. These results underscore the scalability and effectiveness of PSO-optimized LSMs on resource-limited neuromorphic platforms, showcasing both improved classification performance and the advantages of energy-efficient processing. Full article

Hybrid neural networks (HNNs), integrating the strengths of artificial neural networks (ANNs) and spiking neural networks (SNNs), provide a promising solution towards generic artificial intelligence. There is a prevailing trend towards designing unified SNN-ANN paradigm neuromorphic computing chips to support HNNs, but developing platforms to advance neuromorphic computing systems is equally essential. This paper presents the PAIBoard platform, which is designed to facilitate the implementation of HNNs. The platform comprises three main components: the upper computer, the communication module, and the neuromorphic computing chip. Both hardware and software performance measurements indicate that our platform achieves low power consumption, high energy efficiency and comparable task accuracy. Furthermore, PAIBoard is applied in a robot dog for tracking and obstacle avoidance system. The tracking module combines data from ultra-wide band (UWB) transceivers and vision, while the obstacle avoidance module utilizes depth information from an RGB-D camera, which further underscores the potential of our platform to tackle challenging tasks in real-world applications. Full article

Bioinspired approaches tend to mimic some biological functions for the purpose of creating more efficient and robust systems. These can be implemented in both software and hardware designs. A neuromorphic software part can include, for example, Spiking Neural Networks (SNNs) or event-based representations. Regarding the hardware part, we can find different sensory systems, such as Dynamic Vision Sensors, touch sensors, and actuators, which are linked together through specific interface boards. To run real-time SNN models, specialised hardware such as SpiNNaker, Loihi, and TrueNorth have been implemented. However, neuromorphic computing is still in development, and neuromorphic platforms are still not easily accessible to researchers. In addition, for Neuromorphic Robotics, we often need specially designed and fabricated PCBs for communication with peripheral components and sensors. Therefore, we developed an all-in-one neuromorphic system that emulates neuromorphic computing by running a Virtual Machine on a conventional low-power CPU. The Virtual Machine includes Python and Brian2 simulation packages, which allow the running of SNNs, emulating neuromorphic hardware. An additional, significant advantage of using conventional hardware such as Raspberry Pi in comparison to purpose-built neuromorphic hardware is that we can utilise the built-in physical input–output (GPIO) and USB ports to directly communicate with sensors. As a proof of concept platform, a robotic goalkeeper has been implemented, using a Raspberry Pi 5 board and SNN model in Brian2. All the sensors, namely DVS128, with an infrared module as the touch sensor and Futaba S9257 as the actuator, were linked to a Raspberry Pi 5 board. We show that it is possible to simulate SNNs on a conventional low-power CPU running real-time tasks for low-latency and low-power robotic applications. Furthermore, the system excels in the goalkeeper task, achieving an overall accuracy of 84% across various environmental conditions while maintaining a maximum power consumption of 20 W. Additionally, it reaches 88% accuracy in the online controlled setup and 80% in the offline setup, marking an improvement over previous results. This work demonstrates that the combination of a conventional low-power CPU running a Virtual Machine with only selected software is a viable competitor to neuromorphic computing hardware for robotic applications. Full article

Neuromorphic computing, a brain-inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to enhance performance while maintaining power efficiency. Neuromorphic chip architecture requirements vary depending on the application and optimising it for large-scale applications remains a challenge. Neuromorphic chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, and on-device learning. Widely used spiking neuron models include the Hodgkin–Huxley Model, Izhikevich model, integrate-and-fire model, and spike response model. Hardware implementation platforms of the chip follow three approaches: analogue, digital, or a combination of both. Each platform can be implemented using various memory topologies which interconnect with the learning mechanism. Current neuromorphic computing systems typically use the unsupervised learning spike timing-dependent plasticity algorithms. However, algorithms such as voltage-dependent synaptic plasticity have the potential to enhance performance. This review summarises the potential neuromorphic chip architecture specifications and highlights which applications they are suitable for. Full article

Analog neuromorphic prototyping is essential for designing and testing spiking neuron models that use memristive devices as synapses. These prototypes can have various circuit configurations, implying different response behaviors that custom silicon designs lack. The prototype’s behavior results can be optimized for a specific foundry node, which can be used to produce a customized on-chip parallel deep neural network. Spiking neurons mimic how the biological neurons in the brain communicate through electrical potentials. Doing so enables more powerful and efficient functionality than traditional artificial neural networks that run on von Neumann computers or graphic processing unit-based platforms. Therefore, on-chip parallel deep neural network technology can accelerate deep learning processing, aiming to exploit the brain’s unique features of asynchronous and event-driven processing by leveraging the neuromorphic hardware’s inherent parallelism and analog computation capabilities. This paper presents the design and implementation of a leaky integrate-and-fire (LIF) neuron prototype implemented with commercially available components on a PCB board. The simulations conducted in LTSpice agree well with the electrical test measurements. The results demonstrate that this design can be used to interconnect many boards to build layers of physical spiking neurons, with spike-timing-dependent plasticity as the primary learning algorithm, contributing to the realization of experiments in the early stage of adopting analog neuromorphic computing. Full article