搜索结果：Frontiers in computational neuroscience

Enteric infectious diseases claim more than 1 million lives annually and are among the top ten causes of death in children younger than 5 years. Remarkable global investment has been dedicated to enteric infectious disease prevention and control; however, the shifting global health landscape is testing the continuance of progress. To evaluate the current status and guide future interventions, we present the latest epidemiological estimates of enteric infectious diseases from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 and assess progress towards the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea (GAPPD) mortality target of fewer than 20 deaths per 100 000 children younger than 5 years by 2025. We quantified the incidence, mortality, and disability-adjusted life-years (DALYs) of enteric infectious diseases by age, sex, and year across 204 countries and territories from 1990 to 2023. In GBD 2023, the following were considered under the category of enteric infectious diseases: diarrhoeal diseases, enteric fever (typhoid and paratyphoid), invasive non-typhoidal Salmonella spp (iNTS) infections, and other intestinal infectious diseases. We also examined 15 aetiologies contributing to diarrhoeal diseases. Incidence and prevalence were estimated with DisMod-MR (version 2.1), a Bayesian meta-regression tool, drawing on data from systematic reviews, population-based surveys, claims data, and hospital sources. Cause-specific mortality was modelled with Cause of Death Ensemble Modelling based on data from sources including vital registration, mortality surveillance, verbal autopsy, and minimally invasive tissue sampling. Years of life lost and years lived with disability were computed and combined to derive DALYs. For aetiology-specific estimation, population-attributable fractions (PAFs) for 15 pathogens were derived with a counterfactual framework. Point estimates and 95% uncertainty intervals (UIs) were generated from 250 draws from the posterior distribution. In 2023, enteric infectious diseases resulted in an estimated 1·27 million (95% UI 0·963-1·68) deaths globally, declining from 3·69 million (3·04-4·56) in 1990. The global age-standardised mortality rate (ASMR) decreased from 74·1 (62·0-92·9) per 100 000 population to 16·4 (12·6-21·3) per 100 000 population during the same period. Diarrhoeal diseases accounted for most deaths in 2023 (1·11 million [0·811-1·54]), followed by enteric fever and iNTS. South Asia and sub-Saharan Africa remained the most affected regions in 2023, with 599 000 (441 000-882 000) and 501 000 (373 000-648 000) deaths due to enteric infectious diseases, respectively, predominantly from diarrhoeal disease. Rotavirus was the leading cause of all-age diarrhoeal disease deaths (PAF 16·3% [12·0-21·5]), followed by norovirus (10·2% [2·4-17·0]) and Shigella spp (9·3% [5·4-15·2]). Among children younger than 5 years, PAFs of deaths due to diarrhoeal diseases were 40·2% (32·5-48·5) for rotavirus, 24·0% (15·1-36·7) for Shigella spp, and 23·4% (13·7-34·3) for adenovirus. Across 204 countries and territories, 141 met the GAPPD mortality target in 2023. The driving aetiologies among countries that did not meet the target in 2023 varied slightly by GBD super-region, but the highest or second-highest number of deaths in children younger than 5 years were consistently attributed to rotavirus. Astrovirus and sapovirus, newly included in GBD 2023, were responsible for 24 600 (6290-49 000) and 18 800 (4650-44 400) deaths, respectively, in 2023, mainly in children younger than 5 years. Our findings show that mortality and ASMRs of enteric infectious diseases declined substantially between 1990 and 2023. This decline is consistent with the expansion of public health measures and broader socioeconomic development. However, the burden in 2023 remains considerably high, with the highest mortality concentrated in sub-Saharan Africa and south Asia. Considering that more than a quarter of all countries had yet to meet the GAPPD mortality target in 2023, sustained efforts are needed to address the persistent burden in affected countries and to adapt to the changing global health landscape. Gates Foundation.

The neural rejuvenation hypothesis proposes that drugs of abuse reactivate developmental plasticity mechanisms to create abnormally persistent addiction memories. While individual molecular components have been characterized experimentally, the population-level dynamics and their collective contribution to addiction pathophysiology remain poorly understood. To develop a computational framework tracking theoretical synaptic population dynamics during simulated drug exposure and withdrawal, and to demonstrate how coordinated population-level transitions could account for key experimental observations in addiction neuroscience. We constructed a mathematical model tracking four theoretical synaptic populations (adult, juvenile, silent, and matured synapses) using differential equations. The model incorporates two distinct processes: (1) rejuvenation of existing synapses through receptor composition switching, and (2) de novo generation of silent synapses during drug exposure. Critically, the total synapse population is dynamic, increasing during drug exposure due to synaptogenesis and decreasing during withdrawal due to pruning. State transitions are explicitly phase-gated: silent synapse generation occurs only during exposure, while maturation and pruning occur predominantly during withdrawal. Rate constants were derived from experimental time scales reported in the literature, with explicit biological time mapping (1 time unit = 2 h). Simulations involved five intermittent exposures followed by extended withdrawal, with comprehensive parameter sensitivity analysis to assess model robustness across ±50% parameter variations. Initial conditions were fixed to represent the experimentally motivated baseline (adult synapses only); alternative initial states were also tested and did not change qualitative conclusions. The model demonstrated coordinated synaptic population transformations that qualitatively paralleled experimental observations. In simulation, results revealed distinct phases of neural rejuvenation with characteristic population dynamics: adult-to-juvenile conversion during exposure (reaching ~500 juvenile synapses in the model), de novo silent synapse generation (~400 synapses), and progressive maturation during withdrawal (~300 matured synapses). The modeled total synapse population increased dynamically from baseline (1,000) to ~1,400 during exposure due to de novo synaptogenesis, then decreased to ~1,300 during withdrawal due to pruning. NMDA receptor composition shifted from 80% GluN2A to 80% GluN2B during simulated exposure. Memory strength increased continuously through biphasic mechanisms: during exposure, memory formation was driven by enhanced plasticity capacity; during withdrawal, memory strengthening was driven by the maturation flux (the rate of CP-AMPAR recruitment into silent synapses), with saturation preventing unbounded growth. Parameter sensitivity analysis demonstrated robust qualitative behavior across ±50% parameter variations. Comparative simulations with natural rewards (modeled with k genesis = 0) showed minimal rejuvenation effects and attenuated incubation, consistent with experimental observations of drug specificity. This computational framework demonstrates how neural rejuvenation might operate as a population-level phenomenon, with sequential recruitment of different plasticity mechanisms creating robust addiction-related memories. The model generates testable hypotheses and provides a foundation for understanding potential therapeutic intervention windows targeting different phases of rejuvenation.

While perceptual multistability arises from many types of stimuli across different sensory systems, there are common dynamical features that may be rooted in universal organizing principles underlying perception. We probe the fundamental mechanisms responsible for visual multistability using a neuronal network model framework in which a set of realistic images directly drives competing pools of neurons with nonlinear dynamics. Incorporating balanced network architecture, long-range connections from excitatory neurons to inhibitory neurons in competing pools, and a dynamic spiking threshold, the model produces irregular percept switching and replicates key experimental observations regarding dominance durations in binocular rivalry. Using a sequence of short-time observations of neuronal dynamics, we derive a new methodology for reconstructing the dynamic percept that generalizes to an arbitrary number of percepts, suggesting how rivalry, fusion, and interocular grouping may serve as different states in a single decision-making system. The model dynamics illustrate that perceptual alternations are potentially rooted in the breakdown of balance between excitation and inhibition when the spiking thresholds of suppressed neurons become sufficiently small, with more balanced dynamics generally facilitating longer dominance durations. Finally, we apply our model analysis toward characterizing the causes of psychiatric or neurological disorders, such as amblyopia and autism. Increasing the strength of connections manifesting from the pool of neurons associated with the stronger eye in amblyopia, we find the weaker eye experiences shorter dominance durations as found experimentally, supporting the notion that sufficiently imbalanced inter-eye competition prompts the suppression of information from the monocular stimulus corresponding to the weakened eye. Similarly, we show increasing the ratio of excitatory to inhibitory inputs in the network systematically yields longer dominance durations as observed for individuals with autism, and we thus demonstrate support for the excitation/inhibition imbalance hypothesis for autism.

Long-range correlations are a key signature of systems operating near criticality, indicating spatially-extended interactions across large distances. These extended dependencies underlie other emergent properties of critical dynamics, such as high susceptibility and multi-scale coordination. In the brain, along with other signatures of criticality, long-range correlations have been observed across various spatial scales, suggesting that the brain may operate near a critical point to optimize information processing and adaptability. However, the mechanisms underlying these long-range correlations remain poorly understood. Here, we investigate the role of synergistic interactions in mediating long-range correlations in the visual cortex of awake mice. We leverage recent advances in mesoscale two-photon calcium imaging to analyse the activity of thousands of neurons across a wide field of view, allowing us to confirm the presence of long-range correlations at the level of neuronal populations. By applying the Partial Information Decomposition (PID) framework, we decompose the correlations into synergistic and redundant information interactions. Our results reveal that the increase in long-range correlations during visual stimulation is accompanied by a significant increase in synergistic rather than redundant interactions among neurons. Furthermore, we analyse a combined network formed by the union of synergistic and redundant interaction networks, and find that both types of interactions complement each other to facilitate efficient information processing across long distances. This complementarity is further enhanced during the visual stimulation. These findings provide new insights into the computational mechanisms that give rise to long-range correlations in neural systems and highlight the importance of considering different types of information interactions in understanding correlations in the brain.

Despite the increasing representation of women in scientific fields, disparities in research funding allocation remain. This inequity deprives talented women researchers of necessary resources, limiting the diversity of perspectives and ideas, and contributes to the "scissor-shaped curve" seen in neuroscience, where women leave before obtaining senior positions. Data transparency and comprehensive reporting of information on grant winners and applicants, as well as reporting of gender and other intersecting demographics and key metrics, are crucial to effectively evaluate funding equity. However, there is a lack of guidelines on which data funders should report. In this study, we aimed to investigate the transparency of neuroscience funders across Europe, focusing on the European Union, Schengen area, and the United Kingdom. To this end, we developed a Transparent Reporting Scale (TRS), composed of 15 items crucial to facilitate transparent and meaningful reporting, and searched for public data from funders in order to apply the scale and evaluate their transparency in data reporting. Across 32 countries and the European Union as a whole, we identified 39 funders, with 90% sharing publicly available data on funding results. Using the TRS, five funders received a "gold" rating, eighteen a "silver" one, and thirteen a "bronze" rating. Scale scores were significantly correlated with the Gender Equality Index [p = 0.64, 95% CI (0.33, 0.83), p = 0.001] and gross domestic product of the countries where funders are based [p = 0.51, 95% CI (0.20, 0.74), p = 0.003], suggesting that collection and/or publication of funding data may reflect overall commitments to gender equity, and be limited due to resources. Data from only 29% of funders could be disaggregated for the neuroscience category specifically, indicating the difficulty in evaluating equity in our field. We collated all available data into an Open Science Framework repository to enable data sharing and further analyses. The TRS can support funders in adopting transparent, standardized reporting practices in order to support evidence-based progress toward gender equity.

The clinical assessment of patients with Disorders of Consciousness (DoC), ranging from the Vegetative State (VS/UWS) to the Minimally Conscious State (MCS), remains a significant challenge in neurology. Gold-standard behavioral tools are prone to high misdiagnosis rates because they depend on overt motor responses, which may be masked by physical impairments. Consequently, there is an urgent need for objective neurophysiological biomarkers to identify residual awareness. Predictive Processing (PP) is a leading theory that views the brain as a hierarchical inference engine. Under this framework, the brain minimizes "prediction errors" between internal generative models and sensory inputs. Neural signatures of these errors, such as the Mismatch Negativity (MMN), provide a window into the brain's automatic modeling of environmental regularities, serving as a proxy for conscious processing. This systematic review aims to identify and appraise peer-reviewed studies from the past 15 years that apply computational PP models to non-invasive brain signals in DoC patients. It synthesizes evidence for their diagnostic and prognostic utility and identifies methodological hurdles to clinical translation. A systematic synthesis was conducted on 30 peer-reviewed studies. Data regarding population demographics (total N≈2045), paradigms, and computational methods, including multivariate pattern analysis and deep learning, were extracted and appraised. The evidence reveals a transition from simple waveform averaging to high-dimensional decoding of hierarchical prediction errors. Global information-sharing markers effectively distinguish conscious states, while the temporal progression of prediction error signatures in the early stages of coma demonstrates high specificity for predicting awakening. Computational PP models offer a transformative path toward reducing misdiagnosis. Future research must prioritize 24-hour continuous monitoring and multimodal data fusion to translate these theoretical frameworks into viable bedside clinical tools.

An increasing number of studies are currently focusing on "personality neuroscience," a term denoting the research aimed at neuroimaging correlates of inter-individual temperament and character variability. Among other methods, a graph theoretical analysis of the functional connectivity in resting-state functional magnetic resonance imaging data was applied in a study by, reporting novel functional connectivity correlates of personality traits. The current paper presents a conceptual replication of the results of this study and discusses the related challenges, including an extension of the original statistical methods in order to illustrate the effect of the multiple comparison problem. Five personality dimensions were obtained using the revised "Big Five" Personality Inventory, including scores of Extraversion and Neuroticism covered in the original paper. Using a larger sample (84 subjects) with adequate statistical power (ranging from 0.75 to 0.95 across analyses), we failed to replicate any of the nine specific neuroimaging correlates of personality presented by Gao et al. While acknowledging differences in the experimental procedures, we discuss that the lack of replication might be caused by the relatively liberal control of false positives in the original study. Indeed, the original testing scheme leads to an expected count of about 10 false positive observations among all tests; applying this scheme to our data we observed a similar number of positive tests, albeit for different relations. No significant correlations were found in our data when standard family-wise error control was applied. These results illustrate the importance of combining exploration with independent validation, use of large datasets, as well as appropriate control of multiple comparison problem in order to prevent false alarms in research into neural substrates of personality differences. Importantly, our findings do not disprove the existence of a link between personality and the brain's intrinsic functional architecture; but rather suggest that such a link might be even more subtle and elusive than previously reported.

Group-aware learning has recently emerged as a promising paradigm for neuroimaging-based disease diagnosis, as population-level interactions can provide complementary information beyond individual imaging features. However, most existing approaches rely on explicitly constructed graphs, which introduce non-trivial design choices, scalability limitations, and sensitivity to graph topology. By incorporating the design philosophy of participatory interaction, we propose IP-Mamba, a scalable and memory-efficient framework tailored for neuroimaging cohorts that models implicit population interactions without the computational burden of explicit graph construction. IP-Mamba treats a mini-batch of subjects as an unordered set and employs a bidirectional Mamba-based sequence modeling mechanism to capture latent inter-subject dependencies. To address the inherent order sensitivity of sequence models, we introduce a Shuffle Consistency Strategy, which promotes permutation equivariance under random permutations of subject order, thereby aligning the model behavior with the clinically-relevant, set-based nature of population data. This design enables efficient implicit hypergraph modeling while maintaining linear computational complexity with respect to the population size. We evaluate IP-Mamba on the OASIS-1 dataset, focusing on the binary classification of Alzheimer's disease (Normal Controls vs. Abnormal) as an early clinical screening task. To address severe class imbalance and ensure diagnostic stability, we implement a Contextual Population Support Set inference mechanism coupled with a robust hybrid SVM decision layer. Experimental results demonstrate that IP-Mamba achieves a balanced accuracy of 87.84% and maintains a high sensitivity (Recall) of 89% for the minority disease class. Compared to conventional 3D CNNs and Transformer-based baselines, IP-Mamba provides highly competitive diagnostic robustness while maintaining a highly efficient linear O(N) memory scaling without the quadratic computational bottlenecks typical of graph-based attention networks. Comprehensive ablation studies further confirm the necessity of bidirectional modeling and shuffle consistency regularization. Overall, IP-Mamba offers a principled, memory-efficient alternative to explicit graph-based methods, providing a scalable solution for population-aware neuroimaging analysis under imbalanced clinical settings.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by atypical brain connectivity and impaired cognitive flexibility. Electroencephalography (EEG) based microstate analysis provides insight into the rapid temporal dynamics of brain networks, offering potential biomarkers for ASD. This study proposes an interpretable classification framework for ASD diagnosis using multidomain microstate-informed features derived from EEG, integrating temporal, spectral, complexity-based, and higher-order metrics to comprehensively characterize brain dynamics. Resting state EEG data from 56 participants (28 with ASD and 28 neurotypical controls; age range: 18-68 years) from the publicly available Sheffield dataset were preprocessed and segmented into microstates using a data-driven clustering approach. From these microstate sequences, we extracted a rich set of features across four domains: (i) temporal, (ii) spectral, (iii) temporal complexity, and (iv) higher-order metrics. Multiple classifiers were evaluated using 10-fold cross-validation, with hyperparameter tuning via a randomized search. Among all classifiers, XGBoost achieved the highest performance, with an accuracy of 80.87% when utilizing the complete multidomain feature set, significantly outperforming single domain models. Explainable AI analysis using SHapley Additive exPlanations (SHAP) identified the top 20 discriminative features, including fractional occupancy derivative for microstate 3, delta-band power in states 1 and 3, and mean inter-transition interval. Retraining XGBoost on these SHAP-selected features yielded 80.34% accuracy, confirming their robustness as potential biomarkers. Statistical validation via Mann-Whitney U-tests and effect size measures further established their significance. The findings from the study demonstrated that microstate-informed features capturing temporal instability, transition unpredictability, and spectral alterations serve as clinically relevant and interpretable candidate neurophysiological markers of ASD, offering translational potential for objective diagnosis, treatment monitoring, and personalized interventions.

Dopamine signaling has become closely associated with reward prediction errors (RPEs)-the difference between expected and experienced value. Although not without controversy, the dopamine RPE hypothesis is one of the most influential ideas in neuroscience. This review briefly summarizes its origins, empirical foundations, and theoretical development. We begin with early psychological studies which demonstrated that prediction errors, broadly defined, are central drivers of learning. These experiments inspired mathematical models that formalized associative learning rules and informed the development of reinforcement learning algorithms for artificial learning, including the influential temporal difference learning (TDRL) framework, where learning is guided by prediction errors in value or reward. These theoretical proposals converged with neuroscience through the landmark discovery that midbrain dopamine neurons show activity patterns that are strikingly similar to the RPEs proposed in TDRL. The idea that this unique neuronal population, already implicated in several behavioral processes and brain disorders, could encode a computational variable central to reinforcement learning algorithms was a major conceptual shift, and provided a strong framework that allowed for rigorous hypothesis testing. Over the past three decades, increasingly sophisticated experiments have both replicated the core dopamine RPE finding across distinct experimental contexts and revealed important deviations from the canonical model predictions. These exceptions have sparked ongoing debate about how the hypothesis should be enhanced, revised, or replaced. The history of the dopamine RPE hypothesis is a quintessential example of how the integration of theory and experiments can drive progress in neuroscience and offers a template for theoretical-experimental synthesis.

Probabilistic Stimulation Maps (PSMs) are increasingly employed to identify brain regions associated with optimal therapeutic outcomes in Deep Brain Stimulation (DBS). However, their reliability and generalizability are challenged by the limited size of most patient cohorts and the inherent variability introduced by different statistical methods and input data configurations. This study aimed to investigate the geometrical variability of Probabilistic Sweet Spots (PSS) as a function of both the number of patients (nPat) and the number of stimulations per patient (nStim), and to model a stability boundary defining the minimum data requirements for obtaining geometrically stable PSS. Three statistical approaches-Bayesian t-test, Wilcoxon test with False Discovery Rate (FDR) correction, and Wilcoxon test with nonparametric permutation correction-were applied to two patient cohorts: a primary cohort of 36 patients undergoing DBS for Parkinson's Disease (PD), and a secondary cohort of 61 patients treated for Essential Tremor (ET), used to assess generalizability. Stimulation test data was collected intra-operatively for the first cohort and post-operatively for the second one. Geometric stability was evaluated based on variability in PSS volume extent and centroid location. The analysis revealed a non-linear trade-off between nPat and nStim to yield stable PSS. A stability boundary was defined, representing the minimum combinations of nPat-nStim required for anatomically robust PSS. Among the tested methods, the Bayesian t-test achieved stability with smaller sample sizes (∼15 patients) and demonstrated a consistent performance across both cohorts. In contrast, the Wilcoxon-based methods showed variable behavior between cohorts, which differed in symptom type and testing phase (intra-operative testing vs. post-operative screening). The proposed PSS stability boundary provides a practical reference for designing DBS studies and stimulation screening protocols aimed at probabilistic mapping. The Bayesian t-test emerged as a reliable method across both cohorts, supporting its potential in studies with limited sample sizes and scenarios where the method needs to be readily generalized to varying symptoms. These findings underscore the importance of considering both cohort size and stimulation count in probabilistic DBS mapping and call for further investigation into method-specific sensitivities to clinical and procedural factors.

Mechanical forces have recently emerged as critical modulators of neural communication, yet their role in high-level cognitive functions remains poorly understood. Here, we present a biologically inspired spiking neural network model that integrates mechanical tension, vesicle dynamics, and spike-timing-dependent plasticity to examine how tension influences learning, memory, and cognitive operations such as pattern completion, projection, and association. We find that increased tension enhances synaptic efficiency by accelerating vesicle clustering and recovery, resulting in a 67% improvement in memory recall speed and a 17% increase in inter-regional synchrony during projection relative to relaxed states. Conversely, a 20% reduction in tension leads to a 31% decline in memory association performance, highlighting the tension-sensitive accessibility of stored information. The model further reveals that an appropriate balance of inhibition is essential for these tension-driven effects: networks with 20% inhibitory neurons achieve optimal spatial precision in memory encoding and recall, whereas insufficient inhibition allows tension-amplified excitation to spread uncontrollably and degrade recall fidelity. Together, these in silico findings position mechanical tension as a functional neuromodulator and suggest new directions for neuromorphic design and energy-efficient, living computing platforms.

Understanding the neural mechanisms underlying the transitions between different states of consciousness is a fundamental challenge in neuroscience. Thus, we investigate the underlying drivers of changes during the resting-state dynamics of the human brain, as captured by functional magnetic resonance imaging (fMRI) across varying levels of consciousness (awake, light sedation, deep sedation, and recovery). We deploy a model-based approach relying on linear time-invariant (LTI) dynamical systems under unknown inputs (UI). Our findings reveal distinct changes in the spectral profile of brain dynamics-particularly regarding the stability and frequency of the system's oscillatory modes during transitions between consciousness states. These models further enable us to identify external drivers influencing large-scale brain activity during naturalistic auditory stimulation. Our findings suggest that these identified inputs delineate how stimulus-induced co-activity propagation differs across consciousness states. Notably, our approach showcases the effectiveness of LTI models under UI in capturing large-scale brain dynamic changes and drivers in complex paradigms, such as naturalistic stimulation, which are not conducive to conventional general linear model analysis. Importantly, our findings shed light on how brain-wide dynamics and drivers evolve as the brain transitions toward conscious states, holding promise for developing more accurate biomarkers of consciousness recovery in disorders of consciousness.

Subject-to-subject variability is a common challenge in generalizing neural data models across subjects, discriminating subject-specific and inter-subject features in large neural datasets, and engineering neural interfaces with subject-specific tuning. While many methods exist that map one subject to another, it remains challenging to combine many subjects in a computationally efficient manner, especially with highly non-linear features such as populations of spiking neurons or motor units. Consider subjects with trained neural decoders as sources and those without as targets. Our objective is to transfer data from one or more target subjects to the domain of the source subjects to directly apply the source neural decoder such that no target decoder needs to be trained. We propose to use the Restricted Boltzmann Machine (RBM) with Gaussian inputs and Bernoulli hidden units; once trained over the entire feature set of subjects, the RBM allows the mapping of target features on source feature spaces using Gibbs sampling. We also consider a novel computationally efficient training technique for RBMs based on the Fisher divergence, which allows closed-form gradients of the RBM to be computed. We apply our methods to decode turning behaviors from neuromuscular recordings of spike trains from the ten muscles that primarily control wing motion in an agile flying hawk moth, Manduca sexta. The dataset consists of this comprehensive motor program recorded from nine subjects, each driven by six discrete visual stimuli. The evaluations show that the target features can be decoded using the source classifier to classify the visual stimuli with an accuracy of up to 95% when mapped using an RBM trained by Fisher divergence, suggesting that RBMs for multi-cross-subject mapping applications are effective and efficient.

The extracellular potential surrounding neurons is of great importance: it is measured to interpret neural activity, it underpins ephaptic coupling between neighboring cells, and it forms the basis for external stimulation of neural tissue. These phenomena have been studied for decades, both experimentally and computationally. In computational models, variants of the classical cable equation for membrane dynamics and an electrostatic equation for the extracellular field are the most common approaches. Such formulations however, typically decouple the governing equations and therefore neglect the bidirectional coupling between the extracellular (E) space, the cell membrane (M), and the intracellular (I) space. We use a finite element-based Extracellular-Membrane-Intracellular (EMI) approach that solves a fully coupled system to study extracellular stimulation and ephaptic coupling in detailed models of cerebellar Purkinje neurons and neocortical layer 5 pyramidal neurons. We vary the distance to the stimulation source, the amplitude, and the frequency of an external current, and we simulate two-cell configurations to assess ephaptic spike-timing effects, synchronization, and the possibility of direct ephaptic action potential triggering. We find that weak sinusoidal stimulation induces subthreshold membrane oscillations that follow the stimulus frequency, and that constant or sinusoidal extracellular stimulation modulate spike rates and spike timing in a manner that depends on stimulation strength and distance. In two-cell simulations, we find that Purkinje neurons synchronize ephaptically in a distance-and extracellular-conductivity-dependent manner, and that pyramidal neuron spike timing is altered by a neighboring firing cell. Direct ephaptic triggering requires markedly reduced extracellular conductivity relative to bulk values. The results provide quantitative insight into extracellular field-mediated neural coupling and how externally applied fields, such as those used in deep brain stimulation, interact with single-neuron biophysics. The results support the view that ephaptic interactions between neurons are more plausibly expressed as spike-timing modulation and synchronization than as direct excitatory triggering under physiological conditions.

This study investigates the characteristics and underlying patterns of sports media audiences from a human-computer interaction (HCI) perspective using artificial intelligence-based deep learning analysis, with the aim of providing foundational data for the sports media industry. To this end, a novel unsupervised clustering framework, the Column-conditioned Prototype-Enhanced Deep Embedded Clustering (CoPE-DEC) technique, was employed to model and analyze multidimensional viewer experience data derived from sports media consumption contexts. The analysis identified three distinct audience clusters with differentiated behavioral, attitudinal, and value-oriented characteristics. The first cluster, labeled "Sports Value Orientation," was characterized by enhanced concentration during sports viewing, promotion of cooperative skills, motivation for health and exercise, vicarious satisfaction, aesthetic appreciation of sports movements, and admiration for athletes' professional and economic success. The second cluster, termed "Sports Consumption Culture Orientation," exhibited a strong preference for sports broadcasts over entertainment content, frequent consumption of online sports media, active engagement with preferred sports, participation in sports-related tourism and activities, acquisition of sports skills through media, and consumption of sports-related products. The third cluster, identified as "Sports Attitude Orientation," reflected predominantly social and emotional dimensions of sports viewing, including improved social adaptation, relationship formation, group cohesion, stress relief, psychological stabilization, healthy competitive attitudes, and enhanced overall wellbeing. These findings demonstrate that AI-driven deep learning approaches, particularly the CoPE-DEC framework, are effective in uncovering latent audience typologies and preference structures in sports media consumption environments. By integrating HCI principles with advanced clustering techniques, this study offers a methodological contribution to audience analysis research and provides practical implications for audience segmentation, personalized content design, and strategic decision-making in the sports media industry. Future research is encouraged to extend this approach by incorporating diverse AI methodologies and multimodal data sources to further advance interdisciplinary insights at the intersection of HCI, artificial intelligence, and sports media studies.

An information theory-based framework is proposed in attempt to explain insistence on sameness in autism as an instance of a general behavior pattern in which an individual tries to reduce surprise and uncertainty. It offers a new definition of autism as an impairment in which cognitive functions are restricted to discrimination, memorization and prediction of tangible properties of the environment. An analogy between insistence on sameness and constrained minimization of the entropy metric is observed and examined for a set of assumptions that describe cognitive limitations of a person with autism. The metric is given by the formula D H (R, M) = H(R|M)+H(M|R), where R represents sequences of random stimuli, M is a memory that stores and retrieves them, and where H(·|·) denotes their conditional entropies interpreted as surprise and uncertainty, respectively. It is first inferred that to minimize the metric an individual can learn about R (and store that knowledge in M) or can restrict R to the already known M. Then, it is concluded that insistence on sameness is a manifestation of the latter. Moreover, it is shown that the proposed framework: (1) Helps to quantify the concepts of surprise, uncertainty, sensory overload and deprivation, anxiety, comfort zone, disappointment, disorientation, pedantry, rigidness, observance or aberrant precision. (2) Leads to a list of guidelines for learning therapies and daily care routines, and allows them to be defined as optimization algorithms and implemented as programs for robotic live-in caregivers. (3) Can be validated with the help of a Turing test-like approach that requires no experiments involving individuals with autism. The framework-if positively validated-will provide advantages of both theoretical and practical importance: it explains the insistent on sameness as a consequence of cognitive restrictions and offers formal foundations and design guidelines for therapies aimed at improving self-reliance of individuals with autism in basic activities of daily living.

Large neuronal networks demonstrate complex dynamics across multiple scales, ranging from single-neuron excitability and spike-train variability to mesoscopic rhythms and whole-brain activity. Different types of differential equation models have been developed to comprehend these phenomena, connecting deterministic, stochastic, and mean-field descriptions. At the deterministic level, ordinary differential equation (ODE) models, including conductance-based neuron models, neural-mass systems, and whole-brain networks, summarize neural behavior through a reduced set of macroscopic variables. At the population level, mean-field partial differential equation (PDE) models such as Fokker-Planck, age-structured, kinetic, and neural field equations describe the evolution of probability or population densities over membrane-potentials, synaptic states, and other kinetic variables. These PDEs link single-neuron mechanisms to population-level activity and allow one to analyze bifurcations, oscillations and other collective patterns. Stochastic differential equation (SDE) models and their extensions that include jump-diffusion processes and stochastic PDEs (SPDEs) are widely used to describe random membrane fluctuations, irregular spike trains, synaptic plasticity and large-scale variability in neural activity. These stochastic models are also applied to neural data analysis, for example to quantify noise in electro-physiological recordings and to infer latent neural dynamics. Because variability and noise are central in neural systems, we devote more space to stochastic models but always relate them back to the surrounding ODE and PDE frameworks. This hierarchy of ODE, PDE, and SDE-SPDE models shows that the versatility of differential-equation-based approaches in neuroscience offers unified tools for multiscale modeling, neural signal processing, cognitive modeling, and the analysis of noisy neural systems. We also discuss some known numerical and computational approaches, especially for stochastic models and conclude by outlining open challenges, such as multiscale inference, control-oriented formulations and the integration of differential-equation models with modern machine-learning methods.

Neurorehabilitation poses a crucial problem in clinical recovery tasks, particularly for individuals with poor motor functions and neurological impairments, and problems in activities of daily living (ADL). To resolve this, we design a novel model, Rehab-DRLX, with a hybrid deep learning (HDL) framework that combines deep reinforcement learning (DRL) with an explainable transformer model to provide interpretable, accurate prognostic results. The propounded model is designed to effectively process the multimodal data inputs, which include clinical records, sensor-entrenched motion data, and neuroimaging, along with time-dependent recovery patterns from its reinforced representation learning (RRL) module. The RRL module employs a convolutional neural network (CNN) within the DRL agent, which performs spatiotemporal feature encoding and dynamically recovers a policy from its reward-guided learning method. To ensure interpretability, the explainable prognosis transformer (XPT) is utilized, which contains clinical contextual positional encoding and a hierarchical attention mechanism to enable transparent and reliable decision-making. This duality in the Rehab-DRLX architecture enables effective forecasting of the recovery outcomes, including functional independence probability, with both interpretability and accuracy, addressing the drawbacks of conventional black box prognosis tools. The experimental results of Rehab-DRLX show the noteworthy improvements in metrics such as accuracy (94.6%), F1-score (0.93), root mean square (RMSE) (0.082), and mean absolute error (MAE) (0.061) compared to existing studies. The ablation studies reveal the significant contribution of every architectural component and its overall performance. The results show the practical viability of Rehab-DRLX, which not only improves decision-making but also builds clinical trust through explainable insights.