Search Results (435)

Orbital maneuver detection is a core component of space situational awareness. The multi-scale characteristics of satellite orbital behavior and sample imbalance issues lead to challenges in existing methods, including insufficient feature adaptation and limited detection accuracy. This paper proposes an Adaptive Spiking Gating Multi-Scale Liquid State Machine (ASG-MSLSM) for orbital maneuver detection based on variations in satellite orbital parameters. The method integrates multi-scale reservoir pools with different scale-dependent decay factors and Leaky Integrate-and-Fire (LIF) neurons to enhance multi-scale temporal feature extraction capability. A spiking gating network is designed to adaptively learn fusion weights for multi-scale features, replacing traditional fixed equal-weight fusion strategies. During training, weighted binary cross-entropy loss is employed to address class imbalance. Experimental results based on real satellite data demonstrate that the proposed method significantly outperforms baseline models in maneuver detection metrics, achieving higher recall, improving feature separability, and reducing both missed detections and false alarms. These results indicate that the proposed method provides a robust solution for orbital maneuver detection. Full article

Liquid State Machines (LSMs), a reservoir computing model based on recurrent spiking neural networks, provide a powerful framework for solving spatiotemporal classification tasks by leveraging rich temporal dynamics and event-driven processing. Although the traditional LSM formulation assumes a fixed, randomly generated reservoir, recent research has explored optimization strategies to improve liquid dynamics. However, most existing approaches focus primarily on optimizing synaptic connectivity or reservoir structure, while the role of neuron-level parameters remains largely underexplored. This work proposes a neuroevolutionary strategy based on a Genetic Algorithm (GA) that encodes both neuron configurations and their spatial positions, explicitly treating neuron-level parameters as optimization targets. By evolving neuron-specific parameters and spatial positions, the method induces diverse reservoir dynamics. Unlike approaches that directly optimize synaptic weights, the proposed representation maintains an encoding whose dimensionality scales linearly with the number of neurons. The approach was evaluated on four synthetic benchmark tasks, including one Frequency Recognition task and three Pattern Recognition tasks, using compact reservoirs composed of only 20 Leaky Integrate-and-Fire neurons. Despite the small reservoir size, the method achieved state-of-the-art or highly competitive performance, reaching mean accuracies of up to 99.71%. In the most challenging case (PR12), performance improved when the reservoir size was increased to 64 neurons. The method was further evaluated on two real-world datasets, N-MNIST and the Free Spoken Digit Dataset (FSDD), using reservoirs of 300 neurons, achieving 90.65% and 81.47% accuracy, respectively, while using substantially fewer neurons than many existing LSM-based approaches. These results highlight the potential of evolving neuron configurations and spatial organization to produce compact and effective liquid reservoirs. Full article

The capacity of the brain to adapt to experience has long been associated with synaptic plasticity; however, recent evidence demonstrates that experience-driven neural activity also modulates white matter organization through dynamic regulation of oligodendrocyte lineage cells and myelination. Activity-dependent myelination has emerged as a complementary form of neuroplasticity that contributes to circuit efficiency, temporal coordination, and cognitive function. This review aims to examine the neurobiological mechanisms linking cognitive stimulation and activity-dependent neuronal signaling with oligodendroglial dynamics and adaptive myelination. A narrative review of experimental and translational studies was conducted, focusing on evidence from animal models and human research exploring neuron–oligodendroglia interactions, neurotransmitter-mediated signaling, learning paradigms, physical exercise, and neuromodulatory interventions relevant to myelination and brain plasticity. Accumulating evidence indicates that cognitive stimulation, learning, and physical activity modulate neuronal firing patterns and neurotransmitter release, influencing oligodendrocyte precursor cell proliferation, differentiation, and myelin remodeling. Neurotransmitters such as glutamate, GABA, dopamine, and acetylcholine play key roles in neuron–oligodendroglia communication, largely through calcium-dependent intracellular signaling pathways. These mechanisms have been associated with experience-dependent circuit refinement across motor, cognitive, and stress-related paradigms. Rather than implying direct clinical effects, this review highlights oligodendroglial plasticity as a biologically plausible substrate through which cognitive and behavioral experiences may influence adaptive myelination and white matter integrity. Understanding these mechanisms provides a conceptual framework for future research exploring non-pharmacological approaches to modulate brain plasticity at the level of myelin. Full article

Neurogranin (Ng) is a postsynaptic calmodulin-binding protein highly enriched in forebrain neurons and widely implicated in synaptic plasticity. However, whether Ng contributes more broadly to neuronal network maturation and cellular homeostasis remains unclear. Here, we examined the consequences of silencing or restoring Ng to adult physiological levels in primary hippocampal neurons. Ng expression promoted dendritic expansion, increased synaptic number, and shifted the axon initial segment toward the soma, consistent with structural adaptations to enhanced connectivity. Calcium (Ca²⁺) imaging revealed a marked increase in spontaneous neuronal activity and network synchronization, which was confirmed by electrophysiological recordings showing enhanced burst firing and spike synchrony. At the molecular level, Ng altered Ca²⁺/calmodulin (CaM) signaling by increasing total CaM levels, reducing Ca²⁺/CaM-dependent protein kinase II (CaMKII) abundance while increasing its relative autophosphorylation, and downscaling specific ionotropic glutamate receptors. Despite elevated network activity, Ng expression enhanced neuronal metabolic competence and viability, reduced cellular stress signaling and induced modest caspase-3 activation without engagement of apoptotic pathways. Together, these results indicate that Ng promotes neuronal maturation and coordinated network activity while engaging compensatory mechanisms that preserve excitatory balance and neuronal resilience. Our findings identify Ng as a molecular integrator linking Ca²⁺/CaM signaling with the structural and functional maturation of neuronal networks. Full article

The convergence of sensing and processing is a critical frontier in the development of energy-efficient spiking edge intelligence. This paper presents a novel hardware implementation of a sensory neuron evolving from the leaky integrate-and-fire (LIF) model by coupling a volatile memristor with an LC tank circuit. The proposed memristor–resistor–inductor–capacitor (MRLC) neuron embeds electromagnetic sensing directly into neuronal dynamics, enabling direct transduction of proximity information into spike trains. We demonstrate that the circuit functions as a metal-sensitive proximity sensor with spiking output in both simulation and physical experiments. Moreover, the MRLC neuron exhibits rich dynamical regimes, including regular spiking, bursting with 2–5 spikes per burst, and quasi-chaotic behavior, as well as sensing memory provided by hysteresis-like multistability, which is a notable advancement over simple rate-encoding LIF neurons. Full article

HIV-associated neurocognitive disorders (HAND) persist despite combination antiretroviral therapy (cART) and can be exacerbated by repeated cocaine (COC) exposure. Because COC, HAND, and cART independently disrupt medial prefrontal cortex (mPFC) function, their combined neurotoxic impact is a critical clinical concern. Using patch-clamp electrophysiology in HIV-1 transgenic (Tg) and non-Tg rats, we examined mPFC pyramidal neuron activity following repeated exposure to COC and/or cART. In non-Tg rats, COC and cART independently increased neuronal firing, trending toward an additive hyperactive effect when combined. Conversely, HIV-1 Tg rat neurons exhibited plateaued excitability, with no further firing elevations induced by COC or cART. Under intense depolarizing stimuli, treated neurons displayed overactivation-induced firing declines. These findings indicate that while COC and cART additively disrupt mPFC function in non-Tg rats, excitability mechanisms appear saturated in the HIV-1 Tg model. This restricted experimental context highlights the overlapping neurobiological impacts of cART and stimulant use, providing foundational insights into the comorbidity of COC use disorder and HAND. Full article

The 2025 approval of the selective NaV1.8 blocker suzetrigine for acute pain marked a pivotal advance in analgesic drug development. Yet the subsequent failure of Vertex’s next-generation NaV1.8 inhibitor VX993 to demonstrate clinical analgesia underscores enduring challenges in translating mechanistic promise into patient benefit. This review examines why promising targets and compounds, spanning NaV and TRP channels, often falter and outlines a path toward more reliable target selection and validation. I first summarize the pain pathway, from nociceptor transduction through spinal processing to cortical perception, emphasizing how inflammation and peripheral sensitization reshape excitability. Historically serendipitous, pain drug discovery now prioritizes molecular precision. Most approved chronic pain therapies act in the CNS and are limited by modest efficacy and adverse effects. Nociceptor-enriched targets (NaV1.7/1.8/1.9; TRP channels) remain attractive, yet redundancy among NaV subtypes and the necessity of blocking targets at the correct anatomical sites complicate translation. Human genetics and multi-omics provide a powerful, unbiased engine for target discovery. Rare high-impact variants offer strong causal hypotheses, while common polygenic contributions illuminate broader susceptibility. Large biobanks increasingly reveal a mismatch between legacy pain targets and genetically supported candidates across neuronal and non-neuronal cells. Human DRG transcriptomics highlight NaV channel redundancy. Human in vitro electrophysiology and PK/PD analyses show suzetrigine achieves ~90–95% NaV1.8 engagement, yet neurons can still fire unless additional channels are blocked. Species differences and drug distribution (including BBB/PNS penetration and P-gp efflux) critically influence efficacy; centrally accessible blockade (e.g., for NaV1.7 or TRPA1) may be necessary to achieve robust analgesia, challenging peripherally restricted strategies. Osteoarthritis illustrates how obesity-driven metabolic inflammation, synovial immune activation, subchondral bone remodeling, and specific nociceptor subtypes converge to drive mechanical pain. Multi-omic integration across diseased human tissues can pinpoint causal processes and cell types, enabling more selective and safer target choices. I propose a practical framework for target validation that integrates: (i) rigorous human genetic support; (ii) cell-type and site-of-action mapping; (iii) human-relevant electrophysiology and PK/PD with verified target engagement; (iv) species-appropriate models; (v) consideration of modality (small molecule, biologic, RNA, targeted protein degradation). Advancing genetically and anatomically aligned targets, tested at the right sites and exposures, offers the best path to genuinely effective, better-tolerated pain therapeutics. Full article

The inherent bottleneck of the von Neumann architecture and the limited power budget of edge devices necessitate energy-efficient hardware solutions for artificial intelligence. Memristor-based In-Memory Computing (IMC) has emerged as a promising candidate; however, the high-power consumption of peripheral circuits, particularly Analog-to-Digital Converters (ADCs), and the reliability issues of memristive devices remain significant challenges. In this paper, we propose a hybrid Convolutional Spiking Neural Network (CSNN) architecture designed for resource-constrained edge computing. Our approach integrates digital Non-Leaky Integrate-and-Fire (NLIF) neurons with Knowm Self-Directed Channel (SDC) memristor-based synapses in a 1T1R crossbar array. To maximize power efficiency, we replace conventional high-resolution ADCs with a streamlined readout circuit utilizing a Current Sense Amplifier (CSA) and a 1-bit comparator. Furthermore, we employ an intensity-to-latency temporal coding scheme to minimize spike activity and mitigate device endurance degradation. We validated the proposed system using the MNIST dataset, achieving a classification accuracy of 97.8%, which is comparable to state-of-the-art floating-point SNNs using supervised learning methods. Power analysis confirms that our 1-bit readout method consumes only 18.4% of the energy required by an 8-bit ADC-based approach while maintaining negligible accuracy loss. Additionally, the deterministic single-spike nature of our temporal coding significantly reduces write stress on memristors compared to rate coding. These results demonstrate that the proposed hybrid CSNN offers a robust and energy-efficient solution for neuromorphic edge intelligence. Full article

In event-based neuromorphic processing, computer vision finds an efficient alternative capable of optimizing computational and energy resources, inspired by the dynamics of biological neural systems. In the development of real-time processing systems, it is crucial to visually represent the information captured by sensors and to explore its content with precision. Thus, machine learning models are implemented with the capability of being deployed on hardware devices with limited capabilities, depending on the intended purpose, ensuring savings in computational resources. The aim of this work was to evaluate the limits of the implemented neuron model, leaky-integrate and fire (LIF), for fitting convolutional layers of a neural network. To this end, the characteristics of the LIF neuron model used are summarized, as well as the details of its implementation in a hardware design, using configurable parameters. The experimental phase considered two convolution approaches to compare performance, Matlab R2022a software and a spiking convolutional processor for an FPGA, using sample recordings from the MNIST-DVS dataset and Sobel kernels for edge detection. The results reflect that the number of spikes generated by both approaches is very similar and their distribution by frame addresses is directly proportional. Full article

The maxillary sinus is frequently implicated in facial pain syndromes arising from infection, neoplasia, dental procedures, and, importantly, migraine, which can mimic “sinus headache” and contribute to misdiagnosis and inappropriate antibiotic use. Despite the clinical burden of chronic maxillary sinus pain, the sensory neuron subtypes that convey nociceptive and mechanosensory signals from the sinus mucosa remain incompletely defined. In this study, trigeminal ganglion (TG) neurons innervating the maxillary sinus (maxillary sinus TG neurons) were retrogradely labeled with the fluorescent dye DiD in mice and characterized using ex vivo patch-clamp electrophysiology and single-cell RT-PCR. Maxillary sinus TG neurons were found to be predominantly small-diameter, C-afferent nociceptors with electrophysiologic features including high thresholds, repetitive firing, and broad action potentials. Notably, maxillary sinus TG neurons formed a distinct molecular and functional subgroup: they expressed Nav1.9, while showing minimal Nav1.8 expression and limited overlap with Nav1.8-positive nociceptor populations. A majority of maxillary sinus TG neurons were mechanically responsive, generating mechanically activated currents with heterogeneous adaptation profiles, and a subset expressed the mechanoreceptor Piezo2. Collectively, these findings identify maxillary sinus TG neurons as a specialized population of Nav1.9-enriched C-afferent nociceptors with mechanosensitive properties, providing a mechanistic framework for pressure-evoked sinus pain. This work advances the neurobiological basis of sinus-related pain and suggests that Nav1.9 and mechanoreceptor pathways may be potential therapeutic targets for conditions in which sinus symptoms overlap with migraine and other craniofacial pain disorders. Full article

Background: We previously established that striatal, but not cortical, dopaminergic activation stimulates movement, indicating that the crucial and original site of dopaminergic stimulation of motor function is the striatum, not the motor cortex. In the present study, we have further investigated the potential effects of the cortical and striatal dopaminergic activity on cortical pyramidal neuron physiology. Methods and Results: First, under a constant fluorescence imaging condition, we established that DA innervation and D1R and D2R expression were very low in the cerebral cortex but very high in the striatum. Second, we performed cellular neurophysiological experiments on layer 2/3 pyramidal neurons in the primary motor cortex (M1) in tyrosine hydroxylase gene knockout (TH-KO) DA-depleted mice that have hyperfunctional DA receptors. Using brain slice–whole-cell patch-clamping techniques, we found that M1 layer 2/3 pyramidal neurons had lower input resistance, stronger inward rectification, more negative RMP, and fired fewer spikes in DA-depleted TH-KO mice than in DA-intact WT mice; M1 layer 2/3 pyramidal neurons also had a diminished synaptic release function with reduced frequencies for spontaneous and miniature excitatory synaptic currents in TH-KO mice compared to WT mice. Third, we also found that when TH-KO mice were treated with L-dopa before brain slice preparation, these neurophysiological deficits of M1 layer 2/3 pyramidal neurons were reversed, but 30 min incubation of cortical brain slices with 10–20 μM DA produced no detectable effect in M1 layer 2/3 pyramidal neurons in TH-KO mice and WT mice. Fourth, Golgi staining showed that cortical pyramidal neuron morphology was indistinguishable between WT mice and TH-KO mice. Conclusions: Our results indicate that DA loss in the striatum, not in the cortex, indirectly reduces cortical pyramidal neuron membrane excitability and weakens synaptic function. Our data also indicate that (1) the normal direct effects of the cortical DA system on cortical pyramidal neurons are weak, (2) the striatal DA system is the dominant DA system in the brain, and (3) striatal DA activity can indirectly increase cortical neuron activity (spike firing and synaptic activity) and thus critically contribute to brain function. Additionally, our data suggest that in DA depletion rodent PD models, DA loss-induced effects on cortical pyramidal neurons and other neurons are functional rather than structural, such that DA replenishment restores motor function almost instantaneously. These findings provide important insights into how the brain’s dopaminergic system controls our motor and cognitive functions and indicate that the striatum is the main therapeutic target of dopaminergic drugs. Full article

Huwentoxin-XI (HWTX-XI) is a 55-amino acid peptide belonging to the family of spider Kuntiz-type toxins (KTTs), isolated from the venom of the Chinese tarantula Cyriopagopus schmidti. Under whole-cell voltage-clamp conditions, HWTX-XI was found to block Kv1.1 potassium channels but had no effect on other potassium channel subunits (Kv1.4, Kv2.1, Kv3.1 and Kv4.2), sodium channels or calcium channels. In the present study, it was found that the substitution of Tyr379 by the valine in the filter region significantly decreased the affinity of toxin HWTX-XI by about 90-fold, indicating that the Kv1.1 filter region is a critical determinant of HWTX-XI potassium channel activity. After intrathecal or intraplantar injections, HWTX-XI decreased the mechanical nociceptive threshold (hyperalgesia) for a long-lasting period. HWTX-XI also significantly increased the firing frequency in mouse DRG neurons. The novel function of HWTX-XI makes it a new tool for studying the relationship between spider toxins and Kv1.1 channels and suggests that Kv1.1 channels might be a novel potential target for preventing and/or treating neuropathic pain. Full article

Predicting the future parking locations and durations of private vehicles using vehicular edge devices is critical for real-time intelligent transportation services, ranging from instant point-of-interest recommendations to dynamic route planning. Advanced deep neural networks like Transformers demonstrate exceptional performance in mobility prediction; however, their heavy reliance on dense matrix multiplication makes them unsuitable for real-time applications on vehicular edge devices. Spiking neural networks offer a potential solution due to their asynchronous event-driven characteristics and low power consumption. However, existing spiking neural networks face three fundamental challenges: (1) handling heterogeneous inter-event intervals; (2) mitigating quantization errors in regression tasks under limited simulation steps; and (3) efficiently regulating information flow based on external contexts. To address these challenges, we propose an event-driven spiking neural network for private vehicle parking prediction called Spark. First, we design a Time-Adaptive Leaky Integrate-and-Fire neuron with a lookup table-based decay mechanism to efficiently model variable inter-event intervals. Second, an accumulate-based readout strategy is introduced to mitigate quantization errors by integrating discrete spike trains into continuous output values for high-precision regression. Third, a Spiking Contextual Gating module is proposed to selectively regulate spiking information flow across channels based on environmental context. These components are integrated into a unified architecture that maintains high prediction accuracy while remaining computationally efficient. Extensive experiments on real-world datasets demonstrate that Spark achieves an effective balance between prediction accuracy and computational efficiency compared to baselines. Full article

Trigeminal neuralgia and orofacial pain often require effective local anesthesia with minimal side effects. Puerarin (PUE), a major bioactive flavonoid derived from Pueraria lobata, has shown potential analgesic properties. This study aimed to investigate the inhibitory effects of local PUE administration on the excitability of wide-dynamic-range (WDR) neurons in the spinal trigeminal nucleus caudalis (SpVc) and to compare its potency with the conventional local anesthetic lidocaine. Extracellular single-unit recordings were performed on SpVc WDR neurons in anesthetized rats. PUE (1 and 10 mM) or lidocaine (37 mM; 1%) was administered subcutaneously into the peripheral receptive field. Neuronal responses to graded non-noxious and noxious mechanical stimuli were quantified before and after drug application. Local administration of PUE significantly suppressed the mean firing frequency of SpVc WDR neurons in a dose-dependent and reversible manner. The inhibitory effect peaked at 10 min post-injection and recovered within 30 min. Notably, 10 mM PUE exerted an inhibitory magnitude (68.7 ± 6.4%) comparable to that of 37 mM lidocaine (58.1 ± 4.3%), indicating that PUE possesses approximately four-fold the inhibitory potency of lidocaine on a molar basis. The suppressive effect was consistent across both non-noxious and noxious stimulus intensities. These findings provide the first in vivo evidence that PUE effectively attenuates trigeminal nociceptive transmission, likely via the modulation of voltage-gated sodium channels and acid-sensing ionic channels at peripheral nerve terminals. As a natural dietary constituent with high potency and a low risk of systemic side effects, PUR represents a promising candidate for complementary and alternative medicine in the management of orofacial pain, such as temporomandibular disorders and trigeminal neuralgia. Full article

Oxidative stress-induced lipid peroxidation products (LPPs), particularly 4-hydroxy-nonenal (4-HNE) and 4-oxo-nonenal (4-ONE), have recently gained attention for their direct regulation of ion channels essential for pain signaling. In this study, we investigated how these two LPPs affect the electrophysiological properties of neurons, specifically voltage-gated sodium (Na_V) channels, thereby influencing sensory neuron excitability and pain pathways. Using human neuroblastoma (SH-SY5Y) and ND7/23 cells (a fusion cell line exhibiting partial sensory neuron properties), we measured changes in Na_V channel-mediated sodium currents following treatment with 4-HNE or 4-ONE. Whole-cell patch-clamp experiments showed that 4-ONE (10 µM) and 4-HNE (100 µM) did not significantly alter the peak sodium current amplitude in SH-SY5Y cells. However, in ND7/23 cells, both 4-HNE and 4-ONE induced a negative shift in Na_V channel activation voltage dependence, enabling sodium channel activation at lower membrane potentials. Furthermore, current-clamp recordings in primary mouse dorsal root ganglion neurons demonstrated that treatment with 4-ONE and 4-HNE reduced the current threshold required to elicit action potentials and significantly increased action potential firing frequency. These findings indicate that LPPs enhance pain sensitivity by modulating Na_V channels, which play a crucial role in pain transmission. In conclusion, 4-HNE and 4-ONE shift the voltage-dependent activation of sodium channels toward more negative potentials, thereby increasing the excitability of primary sensory neurons and amplifying pain signals. This study provides molecular insights into how oxidative stress-related lipid peroxidation contributes to sensory mechanisms and offers potential avenues for developing new treatments for oxidative stress- or inflammation-associated pain. Full article