Search Results (81)

Artificial neural network and neuron models have made significant contributions to the area of neurodynamics. Investigating the dynamics of artificial neurons and neural networks is vital in developing brain-like systems and understanding how the brain functions. Neural network models and memristive neurons are currently demonstrating a lot of promise in the study of neurodynamics. In order to model the dynamics of biological synapses, this study explores the complex dynamical behavior of a discrete fractional Hopfield-type neural network using a flux-controlled memristive element with periodic memductance. Hyperbolic tangent and sine are the heterogeneous activation functions that are implemented in the proposed system to improve nonlinearity and replicate various forms of brain activity. Stability and bifurcation analyses are used to illustrate the nonlinear dynamical nature of the constructed network model. We examine how the fractional order $\left(\nu \right)$ and periodical memductance aspects influence the dynamics of the system to emphasize the emerging complex phenomena like multi-layered dynamics and the presence of several distinct dynamical states throughout the system variables. Randomness and complexity of the time series data for the proposed system are illustrated with the help of approximate entropy analysis. These findings could help researchers better understand brain-like memory networks, neuromorphic computers, and the theoretical study of neurological and mental abilities. The study of multi-layer attractors can be useful in advanced sensory devices, neuromorphic devices, and secure communication. Full article

This study investigates the ongoing electrical activity of local neural networks—referred to as neurodynamics—across 37 anatomically defined brain regions. We analyzed stereotactic intracranial EEG (sEEG) recordings from 106 subjects during wakeful rest, focusing on scale-free (power-law) properties to determine whether distinct brain regions exhibit unique neurodynamic signatures. Results revealed a power-law regime in two frequency ranges (approximately 0.5–4 Hz and 33–80 Hz). Notably, the power-law exponent (slope) in the high-frequency band differed significantly between cortical and subcortical areas (p < 0.01). These findings suggest that local neurodynamics, as reflected in scale-free characteristics, may serve as a functional “fingerprint” for brain region classification. This approach may contribute to functional brain parcellation efforts and offer new insights into the intrinsic organization of neuronal networks as revealed by resting-state activity analysis. Full article

Motor imagery EEG decoding often relies on static functional connectivity graphs that cannot capture the dynamic, stage-wise reorganization of brain networks during tasks. This paper aims to develop a graph neural network that explicitly simulates this neurodynamic process to improve decoding and provide computational insights. This paper proposes a Structure-Feature Evolution Graph Attention Network (SFE-GAT). Its inter-layer evolution mechanism dynamically co-adapts graph topology and node features, mimicking functional network reorganization. Initialized with phase-locking value connectivity and spectral features, the model uses a graph autoencoder with Monte Carlo sampling to iteratively refine edges and embeddings. On the BCI Competition IV-2a dataset, SFE-GAT achieved 77.70% (subject-dependent) and 66.59% (subject-independent) accuracy, outperforming baselines. Evolved graphs showed sparsification and strengthening of task-critical connections, indicating hierarchical processing. This paper advances EEG decoding through a dynamic graph architecture, providing a computational framework for studying the hierarchical organization of motor cortex activity and linking adaptive graph learning with neural dynamics. Full article

Introduction: Judo is a type of combat sport in which athletes must be able to constantly control their position and maintain a constant dynamic balance to respond to their opponent’s moves. In this scenario, the aim of this systematic review was to evaluate the role of core strength and stability in supporting balance, neuromuscular control, and functional performance-related determinants in judo athletes. Methods: PubMed, Scopus, and Web of Science databases were systematically used for articles published from inception to 4 April 2025, to identify any sort of manuscript indicating judo athletes as its population and core training approaches as the intervention (PROSPERO registry with the code: CRD420251032685). Results: Out of 401 studies, after the removal of 206 duplicates, we screened 195 records. Then, seven articles were included in the systematic review. We found that a strong core might improve balance and neurodynamic control. International-level judokas showed greater trunk extensor strength and less trunk angular displacement. Previous research suggests that core training improves physical fitness, balance, and lower limb recovery; moreover, the lack of core muscle strength might predispose athletes to injury, while solid core stability could ensure good support for the body to perform any movement in a balanced, coordinated, and functional manner. Core stability training and strengthening protocols might also decrease the risk of falling, which could have a beneficial effect on judoka athletes. Conclusions: Despite the wide variety of protocols used for core strengthening, it has been documented that a strong core might improve balance and neurodynamic control of movement during competition. Full article

Traumatic experiences can disrupt one’s sense of safety, self-efficacy, and relationships. Prolonged stress may lead to anxiety, depression, and diminished agency. The embodied, subjective manifestations of trauma call for personalized therapeutic approaches that address symptoms and foster resilience. Group Creative Arts Therapies (CATs) offer relational aesthetic interventions that promote resilience and trauma recovery. Incorporating body-based methods, movement, materials and visual expression, CATs support interoceptive awareness, multisensory integration, embodiment, and emotional–cognitive processing. This article presents a review and conceptual framework of group CAT interventions during wartime, focusing on challenges related to body awareness, self-efficacy, and autobiographical memory. It examines how creative aesthetic approaches help process trauma and strengthen resilience. Drawing on predictive processing accounts of brain function, the article explores the neuropsychological impact of trauma and how creative group work may modulate related brain mechanisms. Creative techniques can foster bodily anchored self-awareness, self-efficacy and processes of traumatic memory reconsolidation. Aesthetic experiences are associated with changes in brain activation and connectivity through processes of embodiment, externalization, and meaning making. On an intrapersonal level, converging evidence highlights the role of sensory and sensorimotor processing, along with the dynamic interplay between Default Mode, Executive Control, and Salience networks, as conceptualized in the Triple Network Model. On an interpersonal level, the literature points to the dynamics of brain and body synchronization, as emerging phenomena during shared creative engagement. These neurodynamics provide a coherent framework for understanding how creative arts-based psychotherapeutic group work can support trauma processing and the cultivation of resilience. Full article

This paper presents a distributed smoothing neurodynamic approach for solving the $L_1$-$L_p$ minimization problem, with application to robust and collaborative multi-view three-dimensional (3D) space localization. To handle the non-Lipschitz continuity gradients, a smooth approximation technique is introduced, yielding a distributed neurodynamic model that integrates classical smoothing neural networks with multi-agents consensus theory. Theoretical analysis guarantees the global convergence of each agent’s state to the optimal solution. The stability and convergence of the proposed approaches are rigorously proved using Lyapunov theory. Numerical experiments on multi-view 3D space localization in the presence of measurement noise demonstrate the method’s effectiveness and practical value for distributed visual computing. Full article

This work focuses on solving the singularly perturbed generalized Hodgkin-Huxley (HH) problem. The HH equation is numerically solved by a collocation approach using third-degree splines. The forward difference technique is utilized for time discretization, while $\theta$-weighted schemes are employed for space discretization. Solving non-linear models using discretization and quasi-linearization results in a set of linear algebraic equations, which are solved using matrices. Furthermore, Von Neumann’s (VN) stability and Spectral Radius (S.R) reveal that the suggested technique is unconditionally stable. To assess the performance and accuracy of this method, absolute error (AE), $L_2$, and $L_{\infty }$ norms are offered. The results align with the literature. Simulation results show that the proposed strategy produces accurate results. Full article

Background: Patients after anterior cruciate ligament reconstruction (ACLR) often exhibit persistent biomechanical deficits, particularly during high-demand tasks like the single-leg drop jump (SLDJ). At approximately six months post-ACLR, patients frequently rely on visual input to compensate for persistent sensorimotor deficits during dynamic tasks, which may lead to altered movement patterns. While visual perturbations have been studied in bilateral jump tasks, their impact on SLDJ biomechanics in ACLR patients remains unexplored. Methods: Patients who were still engaged in rehabilitation and not yet cleared for unrestricted return to sport performed SLDJ under three visual conditions: normal vision, low visual perturbation, and high visual perturbation using stroboscopic glasses. Kinematic and kinetic variables were measured using a 3-dimensional motion analysis system and force platform. Comparisons were made between the ACLR and non-operated limbs, as well as across visual conditions. Results: 24 patients (17 males, 7 females; mean age 25.6 ± 6.3 years, mean height 174 ± 9.0 cm, mean weight 74.7 ± 17.2 kg) were included in the analysis. Knee adduction excursion during landing was significantly affected by visual perturbation (F(2, 46) = 6.55, p = 0.004, η² = 0.019). Post hoc analysis showed that high visual perturbation significantly decreased knee adduction excursion compared to normal vision on the ACLR limb (mean difference 1.499°, SE = 0.388, pBonf = 0.003, Cohen’s d = 0.542). A significant difference was also found between low and high visual perturbation on the ACLR limb (mean difference 1.543°, SE = 0.388, pBonf = 0.002, Cohen’s d = 0.558). No significant changes were observed in the non-operated limb across visual conditions. Conclusions: High visual perturbation significantly altered knee adduction excursion on the ACLR limb, resulting in a shift toward greater knee abduction during landing. No changes were observed in the non-operated limb. These findings support the use of visual perturbation in functional assessment protocols after ACLR to better identify persistent biomechanical deficits that may contribute to reinjury risk. Full article

Portfolio optimization is fundamental to modern finance, enabling investors to construct allocations that balance risk and return while satisfying practical constraints. When transaction costs and cardinality limits are incorporated, the problem becomes a computationally demanding mixed-integer quadratic program. This work demonstrates how principles from biomimetics—specifically, the computational strategies employed by biological neural systems—can inspire efficient algorithms for complex optimization problems. We demonstrate that this problem can be reformulated as a constrained quadratic program and solved using dynamics inspired by spiking neural networks. Building on recent theoretical work showing that leaky integrate-and-fire dynamics naturally implement projected gradient descent for convex optimization, we develop a solver that alternates between continuous gradient flow and discrete constraint projections. By mimicking the event-driven, energy-efficient computation observed in biological neurons, our approach offers a biomimetic pathway to solving computationally intensive financial optimization problems. We implement the approach in Python and evaluate it on portfolios of 5 to 50 assets using five years of market data, comparing solution quality against mixed-integer solvers (ECOS_BB), convex relaxations (OSQP), and particle swarm optimization. Experimental results demonstrate that the SNN solver achieves the highest expected return (0.261% daily) among all evaluated methods on the 50-asset portfolio, outperforming exact MIQP (0.225%) and PSO (0.092%), with runtimes ranging from 0.5 s for small portfolios to 8.4 s for high-quality schedules on large portfolios. While current Python runtimes are comparable to existing approaches, the key contribution is establishing a path to neuromorphic hardware deployment: specialized SNN processors could execute these dynamics orders of magnitude faster than conventional architectures, enabling real-time portfolio rebalancing at institutional scale. Full article

The multi-finger robotic hand exhibits significant potential in grasping tasks owing to its high degrees of freedom (DoFs). Object grasping results in a closed-chain kinematic system between the hand and the object. This increases the dimensionality of trajectory tracking and substantially raises the computational complexity of traditional methods. Therefore, this study proposes the discrete finite-time convergent neurodynamics (DFTCN) algorithm to address the aforementioned issue. Specifically, a time-varying quadratic programming (TVQP) problem is formulated for each finger, incorporating joint angle and angular velocity constraints through log-sum-exp (LSE) functions. The TVQP problem is then transformed into a time-varying equation system (TVES) problem using the Karush–Kuhn–Tucker (KKT) conditions. A novel control law is designed, employing a three-step Taylor-type discretization for efficient implementation. Theoretical analysis verifies the algorithm’s stability and finite-time convergence property, with the maximum steady-state residual error being $O\left({\tau }^3\right)$. Numerical simulations illustrate the favorable convergence and high accuracy of the DFTCN algorithm compared with three existing dominant neurodynamic algorithms. The real-robot experiments further confirm its capability for precise grasping, even in the presence of camera noise and external disturbances. Full article

Hierarchical Reinforcement Learning (HRL) is effective for long-horizon and sparse-reward tasks by decomposing complex decision processes, but its real-world application remains limited due to instability between levels, inefficient subgoal scheduling, delayed responses, and poor interpretability. To address these challenges, we propose Timed and Bionic Circuit Hierarchical Reinforcement Learning (TBC-HRL), a biologically inspired framework that integrates two mechanisms. First, a timed subgoal scheduling strategy assigns a fixed execution duration $\tau$ to each subgoal, mimicking rhythmic action patterns in animal behavior to improve inter-level coordination and maintain goal consistency. Second, a Neuro-Dynamic Bionic Circuit Network (NDBCNet), inspired by the neural circuitry of C. elegans, replaces conventional fully connected networks in the low-level controller. Featuring sparse connectivity, continuous-time dynamics, and adaptive responses, NDBCNet models temporal dependencies more effectively while offering improved interpretability and reduced computational overhead, making it suitable for resource-constrained platforms. Experiments across six dynamic and complex simulated tasks show that TBC-HRL consistently improves policy stability, action precision, and adaptability compared with traditional HRL, demonstrating the practical value and future potential of biologically inspired structures in intelligent control systems. Full article

Background and Objectives: Shoulder dysfunction is a frequent complication after cervical lymph node dissection in patients with head and neck cancer (HNC), leading to pain, reduced mobility, and impaired quality of life. Physiotherapy programs that include strength exercises have shown benefits in managing these sequelae, but the potential added value of neurodynamic mobilization techniques (NDMTs) remains unclear. This pilot randomized controlled trial was designed to examine whether a NDMTs program improves pain and shoulder-related function in HNC survivors with shoulder dysfunction, assessing trajectories during treatment and at short-term follow-up. Materials and Methods: A pilot, assessor-blinded, randomized, parallel-group clinical trial was conducted with 20 participants who had undergone HNC surgery and exhibited shoulder dysfunction. Participants were randomized to either a control group (strength exercises alone) or an experimental group (strength exercises plus NDMTs). Outcomes were assessed at baseline, mid-term (1 week), post-treatment, and 3 months post-treatment. The primary outcome was quality of life measured by the QLQ-H&N35 questionnaire. Secondary outcomes included pain intensity (VAS), disability (DASH), and handgrip strength. Results: Significant improvements were observed in the experimental group for all primary and secondary outcomes. The experimental group demonstrated improved quality of life (p = 0.009), lower pain intensity (p < 0.001), reduced disability (p < 0.001), and increased handgrip strength. Interaction effects for time and group were significant across multiple measures, favoring the NDMTs group. Conclusions: NDMTs are a promising addition to strength programs for improving shoulder dysfunction outcomes in HNC patients, with implications for both clinical practice and future research. Registered in ClinicalTrials: NCT05604235 prior to recruitment. Full article

The aim of this study was to evaluate the efficacy of manual therapy based on neurodynamic techniques and electrophysical modalities in the conservative treatment of cubital tunnel syndrome (CuTS). A total of 128 upper limbs affected by CuTS were initially enrolled in this study, with 82 completing the full treatment protocol. The participants were divided into the following two intervention arms: the first arm (MT) (42 arms) received therapy based on sliding and tensioning neurodynamic techniques, while the second arm (EM) (40 arms) underwent physiotherapy based on electrophysical modalities, specifically low-level laser therapy (LLLT) and ultrasound therapy (US). Chi² and Student’s t-test were used to compare the intervention arms, and no statistically significant differences were found. The evaluated outcomes included nerve conduction testing, ultrasound assessments (measuring cross-sectional area and shear modulus), pain levels, two-point discrimination, thresholds for cutaneous sensory perception, symptom severity, functional ability in specific tasks, and overall post-treatment improvement. Baseline comparisons indicated no statistically significant differences in any measured variables between the intervention groups (p > 0.05). Following treatment, each group exhibited significant improvements in their respective parameters (p < 0.01). Comparisons between groups post-intervention revealed statistically significant differences in nerve conduction results, ultrasound measurements (cross-sectional area and shear modulus), two-point discrimination, and sensory perception thresholds. These parameters improved more in the MT intervention arm. The use of neurodynamic techniques, ultrasound, and low-level laser therapy in the conservative treatment of mild to moderate forms of CuTS has a beneficial therapeutic effect. Full article

Background: Suprascapular neuropathy is a known cause of shoulder pain. Although neurodynamic techniques are widely used to treat peripheral neuropathies, the mechanical behavior of the suprascapular nerve in the shoulder region remains poorly understood. Objectives: This study aimed to analyze the mechanical behavior of the suprascapular nerve during a contralateral cervical glide and an infraspinatus muscle contraction. Methods: The study was conducted in two phases. First, nerve movement was analyzed in 12 cryopreserved cadaveric shoulders using anatomical dissection. Second, suprascapular nerve displacement was assessed in 34 shoulders from 17 healthy volunteers using ultrasound imaging. Results: In cadaveric dissections, the contralateral cervical glide produced a proximal nerve displacement of 1.85 mm at the suprascapular notch. In the ultrasound study, this maneuver resulted in horizontal and vertical displacements of 1.18 mm and 0.39 mm, respectively. In contrast, infraspinatus muscle contraction caused a distal displacement of 3.21 mm in the cadaveric study, and ultrasound imaging showed horizontal and vertical displacements of 1.34 mm and 0.75 mm, respectively. All reported displacements were statistically significant (p < 0.05). Conclusions: The findings of both phases of the study contribute to a better understanding of suprascapular nerve biomechanics and may inform clinical neurodynamic interventions. Full article