Search Results (57)

Background/Objectives: Neuronal oscillations play a key role in the symptoms of Parkinson’s disease (PD). This study investigates the effects of random synaptic inputs, their correlations, and the interaction with synaptic dynamics and spike timing-dependent plasticity (STDP) on the membrane potential and firing patterns of subthalamic nucleus (STN) neurons, both in healthy and PD-affected states. Methods: We used a modified Hodgkin–Huxley model with a Langevin stochastic framework to study how synaptic conductance, random input fluctuations, and STDP affect STN neuron firing and membrane potential, including sensitivity to refractory period and synaptic depression variability. Results: Our results show that random inputs significantly affect the firing patterns of STN neurons, both in healthy cells and those with PD under DBS treatment. STDP, along with random refractory periods and fluctuating input currents, increases the irregularity of inter-spike intervals (ISIs) in output neuron spike trains. Sensitivity analyses highlight the key role of synaptic depression and refractory period variability in shaping firing patterns. Combining random inputs with STDP boosts the correlation between neuron activities. Furthermore, at fixed input noise levels, the model’s output closely matches experimental firing rate and ISI variability data from PD patients and animals, with statistical tests confirming significant effects of STDP on firing regularity. Conclusions: The findings suggest that the stochastic dynamics of STN neurons, combined with STDP, are crucial for shaping neuronal firing patterns in both healthy and PD-affected states. These insights improve our understanding of how noise and plasticity contribute to neural function and dysfunction, with implications for PD symptom management. Full article

This paper introduces a novel neuromorphic-inspired hyper-heuristic framework (NeuHH) for solving the Capacitated Single-Allocation p-Hub Location Routing Problem (CSAp-HLRP), a challenging combinatorial optimization problem that jointly addresses hub location decisions, capacity constraints, and vehicle routing. The proposed framework employs Spiking Neural Networks (SNNs) as the decision-making core, leveraging their temporal dynamics and spike-timing-dependent plasticity (STDP) to guide the real-time selection and adaptation of low-level heuristics. Unlike conventional learning-based hyper-heuristics, NeuHH provides biologically plausible, event-driven learning with improved scalability and interpretability. Experimental results on benchmark instances demonstrate that NeuHH outperforms classical metaheuristics, Lagrangian relaxation methods, and reinforcement learning-based hyper-heuristics. Specifically, NeuHH achieves superior performance in total cost minimization (up to 13.6% reduction), load balance improvement (achieving a load balance factor of as low as 1.04), and heuristic adaptability (reflected by higher heuristic switching frequency). These results highlight the framework’s potential for real-time and energy-efficient logistics optimization in large-scale dynamic networks. Full article

Despite its significance, hysteresis remains underrepresented in mainstream models of plasticity. In this work, we propose a novel framework that explicitly models hysteresis in simple one- and two-neuron models. Our models capture key feedback-dependent phenomena such as bistability, multistability, periodicity, quasi-periodicity, and chaos, offering a more accurate and general representation of neural adaptation. This opens the door to new insights in computational neuroscience and neuromorphic system design. Synaptic weights change in several contexts or mechanisms including, Bienenstock–Cooper–Munro (BCM) synaptic modification, where synaptic changes depend on the level of post-synaptic activity; homeostatic plasticity, where all of a neuron synapses simultaneously scale up or down to maintain stability; metaplasticity, or plasticity of plasticity; neuromodulation, where neurotransmitters influence synaptic weights; developmental processes, where synaptic connections are actively formed, pruned and refined; disease or injury; for example, neurological conditions can induce maladaptive synaptic changes; spike-time dependent plasticity (STDP), where changes depend on the precise timing of pre- and postsynaptic spikes; and structural plasticity, where changes in dendritic spines and axonal boutons can alter synaptic strength. The ability of synapses and neurons to change in response to activity is fundamental to learning, memory formation, and cognitive adaptation. This paper presents simple continuous and discrete neuro-modules with adapting feedback synapses which in turn are subject to feedback. The dynamics of continuous periodically driven Hopfield neural networks with adapting synapses have been investigated since the 1990s in terms of periodicity and chaotic behaviors. For the first time, one- and two-neuron models are considered as parameters are varied using a feedback mechanism which more accurately represents real-world simulation, as explained earlier. It is shown that these models are history dependent. A simple discrete two-neuron model with adapting feedback synapses is analyzed in terms of stability and bifurcation diagrams are plotted as parameters are increased and decreased. This work has the potential to improve learning algorithms, increase understanding of neural memory formation, and inform neuromorphic engineering research. Full article

This work presents a memristive device based on a composite of Scindapsus aureus (SA) and gold nanoparticles (Au NPs), which exhibits excellent resistive switching characteristics and supports multiple forms of synaptic plasticity such as paired-pulse facilitation (PPF), spike-rate-dependent plasticity (SRDP), and spike-timing-dependent plasticity (STDP). The device demonstrates reliable retention, reproducibility, and switching stability. The SA:Au NP composite originates from a natural plant source and possesses green, biodegradable, and biocompatible features, highlighting its potential as a sustainable bio-memristive material for neuromorphic systems. Furthermore, the device exhibits sensitivity to the time interval between paired input pulses, simulating the neural response to interaural time differences (ITDs) in the auditory system. Although not a conventional acoustic sensor, its Δt-responsiveness based on synaptic behavior reveals promising potential in neuromorphic auditory perception and perceptual computing applications. This study provides a foundational synaptic unit for future artificial hearing systems capable of spatial sound localization. Full article

Background/Objectives: Cognitive decline and Alzheimer’s disease (AD) pose a major challenge for the ageing population, with impaired synaptic plasticity playing a central role in their pathophysiology. This article explores the hypothesis that cortico–cortical paired associative stimulation (ccPAS), a non-invasive brain stimulation technique, can restore synaptic function by targeting impaired spike-timing-dependent plasticity (STDP), a key mechanism disrupted in AD. Methods: We reviewed existing studies investigating the effects of ccPAS on neuroplasticity in both ageing and AD populations. Results: Findings suggest age-specific effects, with ccPAS improving motor performance in young adults but showing limited efficacy in older adults, likely due to age-related declines in synaptic plasticity and cortical excitability. In AD, ccPAS studies reveal significant impairments in long-term potentiation (LTP)-like plasticity, while long-term depression (LTD)-like mechanisms appear relatively preserved, emphasising the need for targeted neuromodulation approaches. Conclusions: Despite promising preliminary results, evidence remains limited and largely focused on motor function, with the impact of ccPAS on cognitive domains still underexplored. To bridge this gap, future research should focus on larger and more diverse cohorts to optimise ccPAS protocols for ageing and AD populations and investigate its potential for enhancing cognitive function. By refining stimulation parameters and integrating neuroimageing-based personalisation strategies, ccPAS may represent a novel therapeutic approach for mitigating neuroplasticity deficits in ageing and neurodegenerative conditions. Full article

This paper presents a novel approach to in situ memristive learning by training spiking neural networks (SNNs) entirely within the circuit using memristor emulators in SPICE. The circuit models neurons using Lapicque neurons and employs pulse-based spike encoding to simulate spike-timing-dependent plasticity (STDP), a key learning mechanism in SNNs. The Lapicque neuron model operates according to the Leaky Integrate-and-Fire (LIF) model, which is used in this study to model spiking behavior in memristor-based SNNs. More exactly, the first memristor emulator in PySpice, a Python library for circuit simulation, was developed and integrated into a memristive circuit capable of in situ learning, named the “In Situ Memristive Learning Method for Pattern Classification”. This novel technique enables time-based computation, where neurons accumulate incoming spikes and fire once a threshold is reached, mimicking biological neuron behavior. The proposed method was rigorously tested on three diverse datasets: XPUE, a custom non-dominating 3 × 3 image dataset; a 3 × 5 digit dataset ranging from 0 to 5; and a resized 10 × 10 version of the Modified National Institute of Standards and Technology (MNIST) dataset. The neuromorphic circuit achieved successful pattern learning across all three datasets, outperforming comparable results from other in situ training simulations on SPICE. The learning process harnesses the cumulative effect of memristors, enabling the network to learn a representative pattern for each label efficiently. This advancement opens new avenues for neuromorphic computing and paves the way for developing autonomous, adaptable pattern classification neuromorphic circuits. Full article

A neuromorphic computing network based on SiC_x memristor paves the way for a next-generation brain-like chip in the AI era. Up to date, the SiC_x–based memristor devices are faced with the challenge of obtaining flexibility and uniformity, which can push forward the application of memristors in flexible electronics. For the first time, we report that a flexible artificial synaptic device based on a Ag NPs:a–SiC_0.11:H memristor can be constructed by utilizing aluminum foil as the substrate. The device exhibits stable bipolar resistive switching characteristic even after bending 1000 times, displaying excellent flexibility and uniformity. Furthermore, an on/off ratio of approximately 10⁷ can be obtained. It is found that the incorporation of silver nanoparticles significantly enhances the device’s set and reset voltage uniformity by 76.2% and 69.7%, respectively, which is attributed to the contribution of the Ag nanoparticles. The local electric field of Ag nanoparticles can direct the formation and rupture of conductive filaments. The fitting results of I–V curves show that the carrier transport mechanism agrees with Poole–Frenkel (P–F) model in the high-resistance state, while the carrier transport follows Ohm’s law in the low-resistance state. Based on the multilevel storage characteristics of the Al/Ag NPs:a–SiC_0.11:H/Al foil resistive switching device, we successfully observed the biological synaptic characteristics, including the long–term potentiation (LTP), long–term depression (LTD), and spike–timing–dependent plasticity (STDP). The flexible artificial Ag NPs:a–SiC_0.11:H/Al foil synapse possesses excellent conductance modulation capabilities and visual learning function, demonstrating the promise of application in flexible electronics technology for high-efficiency neuromorphic computing in the AI period. Full article

A spike-timing-dependent plasticity (STDP) device with a Ga-Sn-O (GTO) conductance change layer deposited by a mist-CVD method has been developed. First, the memristive characteristic is analyzed. Next, based on it, spike waveforms are determined. Finally, the STDP characteristic is successfully confirmed. This is an original report on the realization of an STDP characteristic using a thin film deposited by the mist-CVD method, which is achieved by the GTO properties and a well-designed clear methodology to realize a STDP characteristic from a memristive characteristic. Full article

Developing a spiking neural network architecture that could prospectively be trained on energy-efficient neuromorphic hardware to solve various data analysis tasks requires satisfying the limitations of prospective analog or digital hardware, i.e., local learning and limited numbers of connections, respectively. In this work, we compare two methods of connectivity reduction that are applicable to spiking networks with local plasticity; instead of a large fully-connected network (which is used as the baseline for comparison), we employ either an ensemble of independent small networks or a network with probabilistic sparse connectivity. We evaluate both of these methods with a three-layer spiking neural network, which are applied to handwritten and spoken digit classification tasks using two memristive plasticity models and the classical spike time-dependent plasticity (STDP) rule. Both methods achieve an F1-score of 0.93–0.95 on the handwritten digits recognition task and 0.85–0.93 on the spoken digits recognition task. Applying a combination of both methods made it possible to obtain highly accurate models while reducing the number of connections by more than three times compared to the basic model. Full article

In the pursuit of energy-efficient spiking neural network (SNN) hardware, synaptic devices leveraging emerging memory technologies hold significant promise. This study investigates the application of the recently proposed HfO₂/SiO₂-based interface dipole modulation (IDM) memory for synaptic spike timing-dependent plasticity (STDP) learning. Firstly, through pulse measurements of IDM metal–oxide–semiconductor (MOS) capacitors, we demonstrate that IDM exhibits an inherently nonlinear and near-symmetric response. Secondly, we discuss the drain current response of a field-effect transistor (FET) incorporating a multi-stack IDM structure, revealing its nonlinear and asymmetric pulse response, and suggest that the degree of the asymmetry depends on the modulation current ratio. Thirdly, to emulate synaptic STDP behavior, we implement double-pulse-controlled drain current modulation of IDMFET using a simple bipolar rectangular pulse. Additionally, we propose a double-pulse-controlled synaptic depression that is valuable for optimizing STDP-based unsupervised learning. Integrating the pulse response characteristics of IDMFETs into a two-layer SNN system for synaptic weight updates, we assess training and classification performance on handwritten digits. Our results demonstrate that IDMFET-based synaptic devices can achieve classification accuracy comparable to previously reported simulation-based results. Full article

In recent years, implementing reinforcement learning in autonomous mobile robots (AMRs) has become challenging. Traditional methods face complex trials, long convergence times, and high computational requirements. This paper introduces an innovative strategy using a customized spiking neural network (SNN) for autonomous learning and control of mobile robots (AMR) in unknown environments. The model combines spike-timing-dependent plasticity (STDP) with dopamine modulation for learning. It utilizes the Izhikevich neuron model, leading to biologically inspired and computationally efficient control systems that adapt to changing environments. The performance of the model is evaluated in a simulated environment, replicating real-world scenarios with obstacles. In the initial training phase, the model faces significant challenges. Integrating brain-inspired learning, dopamine, and the Izhikevich neuron model adds complexity. The model achieves an accuracy rate of 33% in reaching its target during this phase. Collisions with obstacles occur 67% of the time, indicating the struggle of the model to adapt to complex obstacles. However, the model’s performance improves as the study progresses to the testing phase after the robot has learned. Its accuracy surges to 94% when reaching the target, and collisions with obstacles reduce it to 6%. This shift demonstrates the adaptability and problem-solving capabilities of the model in the simulated environment, making it more competent for real-world applications. Full article

Foundation models trained on vast quantities of data have demonstrated impressive performance in capturing complex nonlinear relationships and accurately predicting neuronal responses. Due to the fact that deep learning neural networks depend on massive amounts of data samples and high energy consumption, foundation models based on spiking neural networks (SNNs) have the potential to significantly reduce calculation costs by training on neuromorphic hardware. In this paper, a visually inspired computational model composed of an SNN and echo state network (ESN) is proposed for the recognition of optic flow. The visually inspired SNN model serves as a foundation model that is trained using spike-timing-dependent plasticity (STDP) for extracting core features. The ESN model makes readout decisions for recognition tasks using the linear regression method. The results show that STDP can perform similar functions as non-negative matrix decomposition (NMF), i.e., generating sparse and linear superimposed readouts based on basis flow fields. Once the foundation model is fully trained from enough input samples, it can considerably reduce the training samples required for ESN readout learning. Our proposed SNN-based foundation model facilitates efficient and cost-effective task learning and could also be adapted to new stimuli that are not included in the training of the foundation model. Moreover, compared with the NMF algorithm, the foundation model trained using STDP does not need to be retrained during the testing procedure, contributing to a more efficient computational performance. Full article

As artificial synapse devices, memristors have attracted widespread attention in the field of neuromorphic computing. In this paper, Al/polymethyl methacrylate (PMMA)/egg albumen (EA)–graphene quantum dots (GQDs)/PMMA/indium tin oxide (ITO) electrically/optically tunable biomemristors were fabricated using the egg protein as a dielectric layer. The electrons in the GQDs were injected from the quantum dots into the dielectric layer or into the adjacent quantum dots under the excitation of light, and the EA–GQDs dielectric layer formed a pathway composed of GQDs for electronic transmission. The device successfully performed nine brain synaptic functions: excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), short-term potentiation (STP), short-term depression (STD), the transition from short-term plasticity to long-term plasticity, spike-timing-dependent plasticity (STDP), spike-rate-dependent plasticity (SRDP), the process of learning, forgetting, and relearning, and Pavlov associative memory under UV light stimulation. The successful simulation of the synaptic behavior of this device provides the possibility for biomaterials to realize neuromorphic computing. Full article

In this study, we investigate the electrical properties of ITO/ZrO_x/TaN RRAM devices for neuromorphic computing applications. The thickness and material composition of the device are analyzed using transmission electron microscopy. Additionally, the existence of TaON interface layers was confirmed using dispersive X-ray spectroscopy and X-ray photoelectron analysis. The forming process of the ZrO_x-based device can be divided into two categories, namely single- and double forming, based on the initial lattice oxygen vacancies. The resistive switching behaviors of the two forming methods are compared in terms of the uniformity properties of endurance and retention. The rationale behind each I–V forming process was determined as follows: in the double-forming method case, an energy band diagram was constructed using F-N tunneling; conversely, in the single-forming method case, the ratio of oxygen vacancies was extracted based on XPS analysis to identify the conditions for filament formation. Subsequently, synaptic simulations for the applications of neuromorphic systems were conducted using a pulse scheme to achieve potentiation and depression with a deep neural network-based pattern recognition system to display the achieved recognition accuracy. Finally, high-order synaptic plasticity (spike-timing-dependent plasticity (STDP)) is emulated based on the Hebbian rule. Full article

This study discusses the potential application of ITO/ZnO/HfO_x/W bilayer-structured memory devices in neuromorphic systems. These devices exhibit uniform resistive switching characteristics and demonstrate favorable endurance (>10²) and stable retention (>10⁴ s). Notably, the formation and rupture of filaments at the interface of ZnO and HfO_x contribute to a higher ON/OFF ratio and improve cycle uniformity compared to RRAM devices without the HfO_x layer. Additionally, the linearity of potentiation and depression responses validates their applicability in neural network pattern recognition, and spike-timing-dependent plasticity (STDP) behavior is observed. These findings collectively suggest that the ITO/ZnO/HfO_x/W structure holds the potential to be a viable memory component for integration into neuromorphic systems. Full article