Search Results (153)

Large-scale spiking neural network simulation requires numerical integration that preserves membrane dynamics and spike timing without making fine-resolution updates prohibitively expensive. This balance is difficult for conductance-based leaky integrate-and-fire (LIF) networks because synaptic decay, threshold crossings, resets, and refractory periods form a hybrid dynamical system. To address this difficulty, we introduce a spike-aware propagation (SAP) approximation method that combines exact receptor-trace updates, analytic homogeneous membrane propagation, Gauss–Legendre quadrature, and spike localization, improving the accuracy–efficiency Pareto frontier. We establish an error bound and conditional convergence under consistent refinement for the proposed SAP. At h = 1 ms, the single-realization T = 1000 ms comparison showed a lower voltage RMSE for SAP than for Euler at the same width in the two high-activity regimes. The five-seed T = 200 ms robustness experiment likewise showed lower voltage RMSE for SAP than for NEST at the same width. At the highest drive, the paired mean reduction was 3.91 mV (95% CI, 3.85–3.97 mV). This quantified gain supports SAP as a practical route to an improved accuracy–efficiency balance in large-scale conductance-based LIF simulation while underscoring the method’s configuration-dependent and regime-dependent scope. Full article

This study investigates the influence of mesh-structured floating-gate (FG) geometry on the electrical and DC synaptic characteristics of flash-memory-based neuromorphic transistors. Devices with mesh sizes of 3 µm × 3 µm, 1 µm × 1 µm, 500 nm × 500 nm, and 200 nm × 200 nm were comparatively evaluated while maintaining the same channel dimensions. As the mesh size decreased, the perimeter-to-area (P/A) ratio increased from 1.33 to 20.0 µm⁻¹, and the cycle-averaged memory window increased from 0.86 to 1.68 V under the same DC program/erase sequence. The 200 nm device also exhibited a read-current modulation range exceeding six orders of magnitude, compared with approximately three orders of magnitude for the 3 µm device. These trends are consistent with a greater contribution of mesh-edge regions to local electrostatic conditions and charge injection. At the same time, smaller mesh sizes produced more abrupt threshold-voltage and read-current changes during the initial program/erase steps, indicating a trade-off between response sensitivity and gradual state modulation. These results show that mesh-size scaling provides an effective geometrical design variable for tuning the memory window and readout characteristics of mesh-structured floating-gate synaptic transistors. Full article

Despite substantial progress in biomarker discovery and multi-omics profiling, several features of Alzheimer’s disease (AD), including prolonged compensated states, heterogeneous clinical trajectories, and marked stage-dependent therapeutic responses, remain difficult to integrate into a single mechanistic framework. In this review, we propose an integrative and testable conceptual framework that reframes AD as a single, progressive multi-layer network disorder whose dynamics arise from hierarchical constraint propagation and progressive loss of cross-scale coordination. Integrating evidence from human genetics, epigenomics, transcriptomics, proteomics, metabolomics, spatial biology, connectomics, and longitudinal biomarker studies, we examine how molecular, cellular, and circuit-level processes interact over time to shape disease progression. Within this framework, different omics measurements are interpreted as complementary representations of disease-related changes, rather than as independent molecular signatures. Disease progression reflects the gradual convergence of immune, metabolic, proteostatic, cytoskeletal, and synaptic stress, with overt cognitive impairment emerging when compensatory capacity is exceeded, producing threshold-like network destabilization. By explicitly linking biological scale, temporal hierarchy, and network structure, this synthesis extends prior network-medicine, connectomic, and multi-omics approaches into a testable framework for state-aware stratification, integrative analysis, and stage-appropriate therapeutic investigation in AD. Full article

Background/Objectives: Neuroinflammation is increasingly recognized as a key mechanism linking infectious diseases with psychiatric disorders through interactions between peripheral immune activation, metabolic pathways, and brain network alterations. This review aimed to synthesize current evidence on the neuroimmune mechanisms and biomarkers underlying infection-associated psychiatric disorders. Methods: A narrative literature review structured according to the SANRA (Scale for the Assessment of Narrative Review Articles) criteria was conducted using the Web of Science Core Collection, PubMed/MEDLINE, Scopus and PsycINFO databases. Boolean search strategies identified studies investigating neuroinflammatory biomarkers, neuroimmune mechanisms, and psychiatric outcomes associated with infectious diseases. The search (January 2022–30 June 2026) included 76 studies in the final qualitative analyses. Results: The reviewed evidence consistently identified inflammatory cytokines and chemokines, complement proteins, blood–brain barrier markers, glial activation biomarkers, neuroaxonal injury markers, kynurenine pathway metabolites, neurotrophic factors, and neuroimaging markers as complementary indicators of infection-induced neuroimmune dysfunction. Across diverse bacterial, viral, parasitic, and systemic infections, these mechanisms converged on peripheral immune activation, blood–brain barrier disruption, microglial activation, kynurenine pathway dysregulation, impaired neurotrophic signaling, synaptic dysfunction, and altered brain network connectivity, contributing to depression, anxiety, psychosis, cognitive impairment, and fatigue. Based on these findings, a unified neuroimmune model integrating peripheral and central mechanisms is proposed. Conclusions: Neuroinflammation emerges as a shared biological pathway linking infections with transdiagnostic psychiatric phenotypes. Although no single biomarker currently demonstrates sufficient diagnostic specificity, integrated multimodal biomarker panels may improve biological stratification, facilitate earlier identification of high-risk patients, and support the development of mechanism-based precision approaches—including candidate anti-inflammatory pharmacological strategies currently under clinical investigation—for infection-associated psychiatric disorders. Full article

Background/Objectives: Epilepsy remains a major neurological disorder, with approximately one-third of patients continuing to experience pharmacoresistant seizures despite the availability of numerous antiseizure medications (ASMs). While current therapies primarily target neuronal hyperexcitability through modulation of ion channels and neurotransmitter systems, increasing evidence suggests that epileptogenesis arises from multiscale interactions involving molecular, cellular, circuit, network, neuroinflammatory, and neurovascular mechanisms. Although therapeutic strategies have diversified, this expanded mechanistic understanding has not yet been fully incorporated into therapeutic development and evaluation. This review integrates current knowledge of multiscale epilepsy pathophysiology with recent therapeutic advances and emerging experimental platforms. Methods: This narrative review synthesized literature identified primarily through PubMed and Google Scholar searches through January 2026, supplemented by targeted updates of therapeutic development and regulatory status through July 2026. Particular emphasis was placed on ion channel modulators, synaptic and neuromodulatory therapies, neuroinflammatory interventions, precision genetic approaches, and human-relevant experimental platforms, including induced pluripotent stem cell (iPSC)-derived models, brain organoids, multi-electrode arrays (MEAs), organ-on-a-chip systems, multi-omics technologies, and artificial intelligence (AI)-based analytical frameworks. Results: Current and emerging therapies target increasingly diverse molecular, circuit, neuromodulatory, and neuroinflammatory mechanisms. However, drug resistance remains multifactorial, and the long-term effects of therapeutic interventions on network remodeling, neuro-glial interactions, and sustained clinical response remain incompletely understood. NAMs provide complementary capabilities for patient-specific disease modeling, functional network phenotyping, neurovascular modeling, and the integration of molecular, electrophysiological, and computational data across biological scales. Conclusions: Epilepsy is increasingly recognized as a multiscale network disorder rather than solely a condition of neuronal hyperexcitability. The coordinated use of complementary human-relevant platforms may help incorporate multiscale mechanistic insights into therapeutic development and evaluation, narrow persistent translational gaps, and support more predictive and mechanism-informed treatment strategies. Full article

Recent years have witnessed a surge in the research of memristors as fundamental building blocks for neuromorphic computing, owing to their exceptional ability to emulate the plastic behavior of biological synapses in a high-density, low-power hardware format. These devices are increasingly recognized as the key to achieving efficient artificial neural networks. Two-dimensional (2D) molybdenum disulfide (MoS₂) is a premier candidate for artificial synapses due to its atomic scale and tunable electronic properties. However, achieving wafer-scale MoS₂ thin films for integrated memristor systems remains a significant challenge. In this work, a scalable strategy combining cetyltrimethylammonium bromide (CTAB)-assisted electrochemical intercalation and oil–water interface self-assembly was developed to fabricate large-area 2H-phase MoS₂ thin films. Leveraging the amphiphilic nature of CTAB-functionalized MoS₂ nanosheets, continuous Janus-structured MoS₂ films with asymmetric wetting properties (hydrophilic vs. hydrophobic) were successfully prepared. Vertical-structured Ag/Janus-structured MoS₂/ITO memristors demonstrated robust non-volatile switching with high endurance and long-term retention. The devices successfully emulated biological synaptic behaviors, including short-term and long-term plasticity. Furthermore, the memristors exhibited distinct optoelectronic synergistic modulation under 405 nm illumination, enabling light-sensitive synaptic functions. This work offers a versatile interface engineering route for low-power integrated sensing–memory–computing hardware. Full article

This paper presents a digitally controlled current-mode synaptic weighting cell in 28 nm FD-SOI CMOS for low-complexity Multi-Layer Perceptron (MLP) inference in event-based Non-Intrusive Load Monitoring (NILM). A compact bias-free [16,16] MLP is trained offline by backpropagation for fixed-weight feedforward inference. Using 616 ON/OFF events extracted from high-frequency REDD measurements, six appliance classes are characterized by four event-level features: active-power variation, reactive-power variation, current total harmonic distortion of the differential event signature, and event interval. With 16-level input quantization, the model achieves 92.9–93.4% test accuracy and 90.5–90.7% test Macro-F1, requiring 320 weighted-sum branches across two hidden layers. A four-input first-hidden-layer weighted-sum unit is selected as a representative circuit instance. Its computation is mapped to bounded current ranges using current-coded inputs, an 8-bit magnitude-controlled current-mode multiplier, sign-bit current steering, differential current accumulation, and signed-current scaling. The circuit contribution focuses on the weighted-sum datapath, particularly the repeated synaptic weighting cell; activation and complete classifier implementation are outside the scope of this work. Transistor-level PVT and supply-variation simulations validate the signed weighted-sum path, while post-layout extraction evaluates the repeated multiplier cell. The results demonstrate the block-level feasibility of digitally programmable current-mode synaptic weighting for compact event-based NILM inference. Full article

Spinal cord injury often causes persistent neurological dysfunction, whereas the developing central nervous system retains greater plasticity. This study investigated whether spontaneous hindlimb motor recovery occurs after complete spinal cord transection in neonatal mice and whether such recovery is accompanied by structural remodeling of the lumbar spinal cord distal to the lesion. C57BL/6J mice underwent complete T9 spinal cord transection at postnatal day 7, and recovery was assessed by Basso Mouse Scale scoring, CatWalk gait analysis, foot-fault testing, immunofluorescence, and electromyography. Compared with adult mice subjected to the same injury, neonatal mice showed progressive partial improvement in hindlimb motor function, including locomotor performance, gait coordination, and foot placement accuracy. Meanwhile, this recovery was accompanied by preservation of lumbar motor neurons and structural changes in the local neural network. Syn⁺ coverage around lumbar motor neurons was relatively preserved in neonatal injured mice compared with adult injured mice; excitatory synaptic input remained increased, and inhibitory input showed a dynamic pattern of early increase followed by later reduction. In addition, myelin-related alterations were less severe, and motor neuron innervation of peripheral muscles (EMG) were relatively preserved in neonatal mice. The results indicate that the restoration of hindlimb motor function after complete spinal cord transection in neonatal mice is associated with structural and functional remodeling of lumbar spinal motor circuits. This includes enhanced synaptic signals around motor neurons and reorganization of the neuromuscular junction. Full article

Alzheimer’s disease (AD) is the most common neurodegenerative disorder and a leading cause of dementia worldwide, characterized by progressive cognitive decline, memory impairment, and neuronal loss. The pathological hallmarks of AD include extracellular accumulation of amyloid-β (Aβ) plaques, intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein, chronic neuroinflammation, oxidative stress, mitochondrial dysfunction, and synaptic degeneration. Current symptomatic therapies provide modest clinical benefits, while recently approved amyloid-targeting monoclonal antibodies, such as lecanemab and donanemab, can slow decline in selected early-stage AD patients but do not cure the disease and are associated with safety, access, and cost concerns. This narrative review summarizes mechanistic evidence from in vitro and in vivo studies and distinguishes preclinical promise from validated clinical utility. Phytochemicals, including polyphenols, flavonoids, alkaloids, terpenoids, and carotenoids, demonstrate neuroprotective effects through antioxidant activity, anti-inflammatory modulation, inhibition of amyloid aggregation, regulation of tau phosphorylation, and support of mitochondria and synapses. Evidence from experimental models suggests that several phytochemicals may help slow AD pathology and improve cognitive function, but clinical translation remains limited due to poor bioavailability, inadequate blood–brain barrier (BBB) penetration, and a lack of large-scale clinical trials. This review highlights critical research gaps and emerging strategies to facilitate phytochemical-based preventive and therapeutic approaches in AD. Full article

We present a unifying thesis which posits that the biological resolution of uncertainty is a fundamental adaptation to entropy’s negative impact on the highly ordered molecular structures required to maintain the living state. These molecular biological adaptations are highly conserved and play a critical role in the survival of the earliest multicellular organisms and the vertebrates thereafter. The imperative to reduce cognitive uncertainty is effected through the dopaminergic Medial Forebrain Bundle (MFB) Reward Prediction Error (RPE) mechanism, or its homologous equivalents, to compute a biological valuation of information. This hypothesis is supported by the central role of the MFB seeking system as the neural substrate of exploratory behavior that leads to the reduction of uncertainty when information is apprehended and cognitively assimilated. We define the human experience of intellectual beauty as the subjective emotional reward that is activated by the MFB seeking system. Accordingly, humans experience intellectual beauty when a high-entropy state of confusion is suddenly resolved into a low-entropy state of insight. In humans, the neuroanatomical substrates of inductive reasoning, inquiry, and the intellectual beauty to which they lead are present at birth. What develops postnatally is synaptic plasticity in the connections among these neurons that is activated in the loving didactic relationship that is established between mother and child during infancy. This dynamic is critically dependent on observational learning on the part of the child. It is supported by the joyful engagement and emotional support of the mother. This provides a paradigm of joy in learning that we propose is the developmental origin of intellectual beauty. This is the reinforcement that maintains inquiring behavior in the search for information that is needed to resist the adverse effects of entropy on life. This paper traces the continuous thread of uncertainty resolution from its phylogenetic origins in associative learning to the intuitive science of early childhood, and ultimately to the highest levels of human inquiry in science, as well as literary, musical and visual arts. The intuitive scientific method gives rise to the collective intelligence of groups, an evolved trait that likely contributed to the success of our hominin ancestors. At the societal level, this collective intelligence scales into the institutional working of markets, driving the macroeconomic price discovery of new information to counter entropy. Importantly, we compare the cost of information across the disparate domains of pharmaceutical drug discovery and the contemporary art market to demonstrate that the imperative to reduce uncertainty manifests as a universal, falsifiable mechanism for the “price discovery” of information. Full article

Idiopathic normal pressure hydrocephalus (iNPH) is a potentially reversible neurological disorder characterized by gait disturbance, cognitive impairment, and urinary incontinence; however, its diagnosis and prediction of shunt responsiveness remain challenging. This systematic review aimed to synthesize current evidence on cerebrospinal fluid (CSF) proteomic biomarkers in iNPH and to identify molecular patterns with diagnostic and prognostic relevance. A PRISMA-guided search of PubMed, Web of Science, and Google Scholar identified 14 eligible studies comprising 1171 iNPH patients. Proteomic analyses revealed substantial heterogeneity in study design and detected proteins; however, consistent patterns emerged. iNPH is associated with upregulation of inflammatory and extracellular matrix-related proteins and relative downregulation of synaptic and neuronal markers. Neurodegenerative proteins, including amyloid-β, tau, and neurofilament light chain, demonstrated value in differentiating iNPH from comorbid neurodegenerative diseases and in predicting response to ventriculoperitoneal shunting (VPS). These findings support a multifactorial model of iNPH involving impaired glymphatic clearance, neuroinflammation, blood–brain barrier dysfunction, and mechanical axonal stress. Multidimensional biomarker profiles, rather than single proteins, appear to provide the greatest clinical utility, highlighting the need for standardized proteomic panels and integrative predictive models. However, given the substantial heterogeneity of the included studies and the predominantly exploratory nature of current proteomic evidence, the identified proteins should be interpreted as candidate biomarkers rather than clinically validated diagnostic or prognostic tools. Multidimensional biomarker profiles appear biologically plausible and may offer greater explanatory value than single proteins, but their clinical utility requires validation in standardized prospective cohorts. The authors therefore propose a conceptual iNPH proteomic “Vulnerability Model” integrating CSF biomarkers to reflect the balance between reversible and irreversible pathology; this is currently a hypothetical model that requires rigorous statistical and clinical validation through large-scale prospective cohort studies before it can fulfill its potential for improving patient stratification and prediction of postoperative outcomes. Full article

SYNGAP1-related intellectual disability presents a therapeutic paradox where genetic rescue is highly effective in neonates but limited in adults, suggesting that deficiency represents a developmental trajectory violation rather than a static biochemical defect. By synthesizing molecular, biophysical, and clinical evidence, this review proposes the “Synaptic Clock” framework, redefining SynGAP1 as a critical developmental regulator. We hypothesize that SynGAP1 operates through a strictly ordered temporal sequence: Phase I (Scaffold Assembly) utilizes the α1 isoform and phase separation to establish the structural postsynaptic density, while Phase II (Catalytic Refinement) involves isoform switching to enable activity-dependent plasticity and homeostatic scaling. This model characterizes synaptic maturation as a biophysical transition from a fluid scaffold to a consolidated gel, potentially marking the biological closure of structural rescue windows. Based on this hypothesized temporal mapping, we establish a phase-stratified therapeutic roadmap—transitioning from early-stage “reset” strategies like gene replacement to late-stage “refinement” and “compensation” via pharmacological and neuromodulatory interventions. Ultimately, validating phase-specific biomarkers, including gamma oscillations and isoform stoichiometry, is essential for shifting from generic interventions toward precision, phase-matched medicine for neurodevelopmental timing. Full article

Motoneurons are under strong pressure to maintain stable motor output throughout an individual life, through homeostatic regulation of their electrical properties. Dysregulated spinal motoneuron excitability has long been implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS). Recent work in SOD1^G93A mice suggests that the homeostatic response of motoneurons becomes dysregulated as cellular processes are disrupted by the disease, causing fluctuations in motoneuron electrical properties. Yet, few studies directly test whether ALS motoneurons respond differently than wild-type motoneurons to a common chronic perturbation. Here, we used in vivo electrophysiology to test whether motoneurons from pre-symptomatic SOD1^G93A mice modulate excitability differently than wild-type motoneurons in response to the same homeostatic perturbation: chronic inhibition exerted by the benzodiazepine diazepam. Using linear mixed-effects statistical models, we assessed whether diazepam treatment differentially modulated passive properties, firing behavior, spike properties, and/or synaptic inputs in SOD1^G93A versus wild-type motoneurons. We identified a significant genotype × treatment interaction effect selectively for properties related to passive membrane integration and spike initiation, including membrane time constant, peak input resistance, and recruitment current. In contrast, firing gain, spike waveform characteristics, and synaptic inputs were largely unaffected. These findings indicate that sustained inhibitory perturbation selectively triggered overactive intrinsic compensatory mechanisms in SOD1^G93A motoneurons rather than inducing widespread changes in firing or synaptic transmission. Together, our results provide direct evidence for over-active homeostatic control of motoneuron excitability and support a view of motoneuron dysfunction in ALS as a problem of altered feedback regulation rather than simply hyper- or hypo-excitability. Full article

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by complex interactions among genetic, environmental, and biological factors. Increasing evidence suggests that immune system processes intersect with neurodevelopment in ways that may influence brain maturation, synaptic organization, and large-scale network function. However, existing literature is often fragmented across molecular, cellular, and systems levels, limiting the development of a coherent interpretive framework. In this review, we propose a developmental neuroimmune cascade model of ASD, in which early-life immune perturbations, arising from prenatal or perinatal factors, may interact with genetic susceptibility to influence cytokine signaling, microglial function, blood-brain barrier dynamics, and gut-immune communication. These processes may, in turn, affect synaptic pruning, excitatory-inhibitory balance, and the maturation of neural circuits, contributing to alterations in large-scale brain networks implicated in sensory processing, interoception, and social cognition. We synthesize evidence from observational human studies, postmortem analyses, and experimental animal models to examine how immune-related mechanisms may contribute to neurodevelopmental trajectories associated with ASD, while explicitly distinguishing associative findings from mechanistic inference. Particular attention is given to the role of distributed network vulnerability, including, but not limited to, insula-centered systems that integrate internal bodily states with affective and cognitive processing. Finally, we consider implications for biomarker development and stratified intervention approaches, emphasizing the importance of developmental timing, biological heterogeneity, and cautious interpretation of translational potential. Rather than positioning immune dysfunction as a singular cause of ASD, this model conceptualizes neuroimmune processes as modulators of developmental trajectories, offering a structured basis for future research linking immune signaling to circuit-level and behavioral outcomes. Full article

Cognitive disorders, including Alzheimer’s disease, Parkinson’s disease, schizophrenia, depression, and vascular dementia, are associated with dysregulation of neurotransmitter systems, including acetylcholine, dopamine, serotonin, glutamate, and γ-aminobutyric acid (GABA). These disorders are increasingly recognized as multifactorial conditions involving oxidative stress, neuroinflammation, mitochondrial dysfunction, synaptic impairment, blood–brain barrier disruption, metabolic imbalance, and gut–brain axis dysregulation. Current pharmacological therapies may provide symptomatic relief; however, their clinical benefits are often limited and associated with adverse effects. Herbal medicines have gained increasing attention as potential complementary approaches for cognitive support and neuroprotection. Preclinical evidence and emerging clinical studies suggest that herbal bioactive compounds may exert neuroprotective effects through antioxidants, anti-inflammatory, and neurotransmitter-modulating mechanisms. Medicinal herbs such as Bacopa monnieri, Withania somnifera, Ginkgo biloba, Glycyrrhiza glabra, Moringa oleifera, and ginseng have shown potential cognitive benefits in experimental models and selected human studies. Advanced delivery systems, including nanoparticles and phytosomes, may further improve the bioavailability and brain-targeting efficiency of herbal compounds. However, current clinical evidence remains heterogeneous and limited by insufficient standardization, small sample sizes, and short study durations. Further large-scale clinical studies and standardized safety assessments are essential before herbal neurotherapeutics can be widely applied in cognitive and neurological disorders. Full article