Frontiers in Computational Neuroscience最新文献

{"title":"On the optimal temporal resolution for information representation in neural activity: a theoretical analysis.","authors":"H Fareed Ahmed, Toktam Samiei, Erfan Nozari","doi":"10.3389/fncom.2026.1885975","DOIUrl":"10.3389/fncom.2026.1885975","url":null,"abstract":"<p><strong>Introduction: </strong>Although neural activity is organized across multiple temporal and spatial scales, the principles determining information representation across scales remain unclear. In particular, while recent empirical results have reported mesoscale optimality in neural decoding, no theoretical accounts exist that can explain when and why such intermediate scales emerge as optimal. Here, we develop an analytical framework to determine optimal temporal scales of neural information representation and their dependence on signal and noise dynamics.</p><p><strong>Materials and methods: </strong>We formulate a multiscale model where neural population activity is represented by temporally encoded trial vectors at micro-, coarse meso-, fine meso- and macroscale resolutions. Neural responses are modeled as stimulus-dependent mean activations corrupted by temporally correlated noise, with signal and noise autocorrelation decay rates varied parametrically. Representational quality is quantified using the sensitivity index (d-prime), measuring the ability of an optimal decoder to distinguish stimulus conditions.</p><p><strong>Results: </strong>We derive closed-form expressions for the sensitivity index at each temporal scale and identify signal and noise autocorrelations as key determinants of decodability. We then validate our theoretical predictions against empirical decodability estimates from synthetic neural data. Comparing these expressions under various combinations of signal and noise autocorrelations across time reveals two main regimes. First, when signal and noise correlations are absent or persistent over time, the optimal resolution falls at one of the two extremes: macroscale (resp. microscale) if signal autocorrelations are significantly stronger (resp. weaker) than noise autocorrelations. When both signal and noise autocorrelations decay, temporal integration creates a trade-off: moderate integration improves decodability by suppressing noise while preserving coherent signal, whereas excessive integration degrades signal and decodability. Therefore, only in the latter regime, mesoscale representations emerge as the optimal regime across a broad range of biologically plausible parameters.</p><p><strong>Discussion: </strong>This work provides a theoretical explanation for how optimal temporal scales depend on the interplay between signal and noise autocorrelations. The framework establishes temporal integration as a principled mechanism linking multiscale neural dynamics to information representation, explains when preprocessing operations such as binning and smoothing enhance or degrade decodability, and provides testable predictions across recording modalities and neural systems.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1885975"},"PeriodicalIF":3.3,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13538443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148886849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing rescaling, discretization, and linearization in RNNs for neural system modeling.","authors":"Mariano Caruso, Cecilia Jarne","doi":"10.3389/fncom.2026.1760701","DOIUrl":"10.3389/fncom.2026.1760701","url":null,"abstract":"<p><p>Recurrent Neural Networks (RNNs) are widely used to model neural activity in Computational Neuroscience. Here, we explore the mathematical foundations of three fundamental procedures that can be implemented: temporal rescaling, discretization, and linearization. These techniques provide crucial tools for characterizing the behavior of RNNs, offering insights into their temporal dynamics, facilitating practical computational implementation, and allowing for linear approximations for analysis. We discuss the flexible order in which these procedures can be applied, emphasizing their importance in modeling and analyzing RNNs for neuroscience and formally prove that these three operations commute pairwise. We also explicitly describe the conditions under which these procedures can be considered interchangeable. Our findings directly inform the design of biologically plausible <i>RNN</i> models for simulating neural dynamics observed in decision-making circuits and motor control, where temporal scaling and stability are critical for matching experimental recordings. Furthermore, we show that this exact commutativity guarantees the structural preservation of the network's controllability, preventing the emergence of inaccessible state-spaces under numerical discretization or temporal rescaling.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1760701"},"PeriodicalIF":3.3,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13534066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148879522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural and dynamical strategies to prevent runaway excitation in reservoir computing.","authors":"Claus Metzner, Achim Schilling, Andreas Maier, Thomas Kinfe, Patrick Krauss","doi":"10.3389/fncom.2026.1845838","DOIUrl":"10.3389/fncom.2026.1845838","url":null,"abstract":"<p><p>Reservoirs, typically implemented as recurrent neural networks (RNNs) with fixed random connection weights, can be combined with a simple trained readout layer to perform a wide range of computational tasks. However, increasing the magnitude of reservoir connection weights to exploit non-linear dynamics can cause the network to develop strong spontaneous activity that drives neurons into saturation, dramatically degrading performance. In this work, we investigate two distinct countermeasures against such runaway excitation. The first approach introduces a subtle non-homogeneous structure into the matrix of connection weights. <i>w</i> _<i>ij</i> , without altering the overall probability distribution <i>p</i>(<i>w</i>). We identify several favorable structuring principles, such as creating a small subset of neurons with weaker-than-average input connections. Even if the rest of the reservoir falls into runaway saturating behavior, this weakly coupled subset remains in a mildly non-linear regime whose dynamics can still be exploited by the readout layer. The second approach implements a form of automatic gain control (AGC), in which a dedicated control unit dynamically regulates the reservoir's average global activation toward an optimal setpoint. Although the control unit modulates the excitability of the reservoir only via a global gain factor, this mechanism substantially enlarges the dynamical regime favorable for computation and renders performance largely independent of the underlying connection statistics.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1845838"},"PeriodicalIF":3.3,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148850258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking spatially distributed neuronal activation overlap to the limits of perceptual discrimination in rodent primary somatosensory cortex.","authors":"Madison Jiang, Joseph J Pancrazio, Thomas J Smith","doi":"10.3389/fncom.2026.1876230","DOIUrl":"10.3389/fncom.2026.1876230","url":null,"abstract":"<p><strong>Introduction: </strong>Intracortical microstimulation (ICMS) of the primary somatosensory cortex can evoke localized tactile percepts, yet the spatial factors that influence perceptual discrimination remain poorly defined. In prior work, we showed that discrimination accuracy between behaviorally evaluated ICMS-evoked percepts declines as stimulation sites converge across cortical depths and adjacent cortical columns. Those results suggest that overlap in neuronal recruitment may constrain perceptual differentiation.</p><p><strong>Methods: </strong>Here, we combine simulated data from a biophysically realistic computational model of the somatosensory cortex with previously collected behavioral data from rats to quantify how overlap in ICMS-evoked activation volume relates to discrimination performance. Within the model, ICMS patterns investigated behaviorally were simulated, and activation volumes were estimated by fitting a range of 50-100% capture ellipsoids to the spatial distribution of activated somata. Overlap in activation volumes between pairs of ICMS patterns was then quantified using the intersection-over-union (IoU) metric.</p><p><strong>Results: </strong>Across both single- and four-shank microelectrode array configurations, we found that discrimination accuracy decreased in an exponential decay-like relationship (<i>R</i> ² = 0.88) as model-derived activation volume overlap increased. Independent of depth vs. lateral separation between ICMS pattern pairs, minimal overlap (IoU < 1%) was associated with high discrimination accuracy (>70%; average of 85%), whereas IoU values exceeding 20% corresponded to near-chance performance.</p><p><strong>Discussion: </strong>These results suggest that ICMS-evoked activation volume overlap between stimulation sites may provide mechanistic insight into the spatial limits of perceptual discrimination in ICMS applications. More broadly, these findings may help guide future investigations aimed at determining appropriate electrode spacing and stimulation strategies for sensory neuroprosthetic design.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1876230"},"PeriodicalIF":3.3,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148850247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational analysis of heart rate variability in ASD and ADHD: a systematic review.","authors":"Raul-Alexandru Gorgan, Teodor-Traian Ştefǎnuţ, Dorian Gorgan","doi":"10.3389/fncom.2026.1911271","DOIUrl":"10.3389/fncom.2026.1911271","url":null,"abstract":"<p><p>Heart rate variability (HRV) represents a rich physiological signal that captures the computational dynamics of autonomic regulation. Emerging evidence suggests that atypical autonomic control contributes to the neurocognitive phenotype of neurodevelopmental disorders (NDD), including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). However, the characterization of HRV alterations in these populations remains incomplete. This review was motivated by the broader research context of the EMPOWER project, funded through the Horizon Europe program, which investigates technology-supported approaches for children with NDD, including smartwatch-based physiological sensing and machine-learning analysis of heart rate, HRV, skin temperature, and photoplethysmography signals during cognitive and relaxation tasks. The present manuscript does not report EMPOWER empirical data. Instead, it systematically synthesizes previously published studies on HRV in pediatric ASD and ADHD. The review examines how HRV has been acquired, processed, modeled, and interpreted in pediatric NDD research, with particular attention to nonlinear signal properties, computational approaches, and interactions between autonomic regulation and cognitive processes. Following PRISMA guidelines, the review surveyed publications over the past decade and included 24 empirical studies from five major databases. ASD studies most often indicated altered vagally mediated HRV and autonomic reactivity, whereas ADHD studies more often suggested task-dependent changes linked to attentional control. Cross-study comparability was constrained by substantial heterogeneity in preprocessing pipelines, feature extraction procedures, and analytical frameworks. Despite these methodological inconsistencies, current evidence supports HRV as a promising physiological signal marker of autonomic dysregulation in pediatric NDD. Nevertheless, the field requires standardized signal processing protocols and reproducible modeling approaches to elucidate the mechanistic and predictive relevance of HRV in ASD and ADHD.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1911271"},"PeriodicalIF":3.3,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13518321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148839400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explicit encoding of temporal dynamics for interpretable melody similarity modeling: insight into machine learning models.","authors":"Yilin Zhang","doi":"10.3389/fncom.2026.1773922","DOIUrl":"10.3389/fncom.2026.1773922","url":null,"abstract":"<p><strong>Introduction: </strong>Temporal melody similarity is a fundamental problem in music modelling, and current methods are mostly based on recurrent or attention-based architectures that implicitly learn sequential structures.</p><p><strong>Methods: </strong>This work adopts an explicit feature-representation approach using temporal-style descriptors, including first-order deltas, relative ratios, polynomial interactions, and rolling statistical features. These features are represented in a model-agnostic manner to enable controlled comparison, interpretability, and reproducible evaluation across architectures. Seven model families were evaluated, including linear baselines, ensemble methods, recurrent networks, attention-based models, and dense fusion architectures, using a large-scale melody similarity dataset containing over 11,000 samples. Cross-validation and bootstrap-based uncertainty estimation were applied for performance evaluation.</p><p><strong>Results: </strong>Results show that the engineered temporal feature space exhibits significant nonlinearity, with the highest performance achieved by Deep GRU (<i>R</i> ² = 0.863, MAE = 0.058), followed by XGBoost among non-recurrent models (<i>R</i> ² = 0.824, MAE = 0.091). Baseline models showed lower performance, demonstrating the nonlinear characteristics of the task. Feature attribution analysis identified temporal descriptors and embedding-based variables as the most influential features.</p><p><strong>Discussion: </strong>The results demonstrate that TFR provides an interpretable and reproducible foundation for evaluating future sequence-learning architectures, particularly for data-constrained or deployment-oriented applications.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1773922"},"PeriodicalIF":3.3,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13506812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148826679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Representational recoding and capacity limits: a conceptual reinterpretation of Sidney Smith's experiment.","authors":"Mikhail Inyushin","doi":"10.3389/fncom.2026.1878543","DOIUrl":"10.3389/fncom.2026.1878543","url":null,"abstract":"<p><p>George A. Miller's classic discussion of memory capacity and Sidney Smith's recoding experiments demonstrated that cognitive limits depend more strongly on the number of active representational units (\"chunks\") than on the total amount of raw information being processed. Here, we reinterpret Smith's experiments from the perspective of modern computational neuroscience and representation learning. We argue that Smith's recoding procedure illustrates a general principle of representational recoding, whereby learning increases the amount of information associated with each active representational unit without increasing the number of units available to the system. This principle provides conceptual links among classical theories of chunking, modern representation learning, vector symbolic architectures, and biological memory systems. The framework may offer useful guidance for future neuromorphic systems operating under strict energetic and structural constraints.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1878543"},"PeriodicalIF":3.3,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13505297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148817865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Arts engagement, active inference, and allostatic regulation: a neurobiological framework for understanding the health effects of aesthetic experience.","authors":"Pier Luigi Sacco","doi":"10.3389/fncom.2026.1852750","DOIUrl":"10.3389/fncom.2026.1852750","url":null,"abstract":"<p><p>A growing body of evidence indicates that arts engagement produces measurable effects on mental and physical health, yet a mechanistic account linking aesthetic experience to physiological regulation remains lacking. This review proposes a neurobiological framework grounded in two convergent theoretical traditions: the active inference formulation of brain function and the allostatic model of physiological regulation. Both frameworks describe organisms as anticipatory systems that maintain viability through model-based predictive control. When active inference chronically fails as in sustained stress, trauma, or learned helplessness, the resulting allostatic load produces systemic metabolic dysregulation and disease vulnerability. This review argues that arts engagement provides contexts optimally suited to restoring effective active inference across sensorimotor, cognitive, affective, and social timescales, thereby recalibrating allostatic regulation. Arts engagement is positioned not as the only activity capable of producing such effects, but as a distinctive and potentially major instance of structured agentic experience, distinguished by its simultaneous engagement of multiple levels of the predictive hierarchy, its intrinsic epistemic motivation, and its capacity to sustain graded uncertainty without pragmatic consequence. A schematic computational specification of the framework is provided, identifying the generative model structure, precision parameters, and expected free energy decomposition relevant to arts-mediated allostatic recalibration. The framework is systematically compared with six alternative accounts of arts-health effects, showing where predictions converge and diverge. Developmental and lifespan perspectives are elaborated, with specific predictions for critical-period effects. The framework generates directional hypotheses for metabolomic, neuroendocrine, and neuroimaging studies, including predictions that distinguish the proposed mechanism from generic stress reduction.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1852750"},"PeriodicalIF":3.3,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13503608/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148817874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the cognitive effects of eye movement desensitization and reprocessing: proposed active mechanisms.","authors":"Tammy Soliman, Matthew Heller, Samuel Girguis","doi":"10.3389/fncom.2026.1751744","DOIUrl":"10.3389/fncom.2026.1751744","url":null,"abstract":"<p><strong>Introduction: </strong>Post-traumatic stress disorder (PTSD) is characterized by intrusive memories and an impaired resilience framework, which often leads to chronic psychological distress. Eye Movement Desensitization and Reprocessing (EMDR) is an emerging therapeutic approach targeting PTSD, yet the precise neurobiological mechanisms remain inadequately defined. Recent studies suggest immune modulation, mainly through molecules such as Interleukin-6 (IL-6), Interleukin-10 (IL-10), Neuropeptide Y (NPY), and Oxytocin (OXY), as well as additional proteins, may play a key role in longitudinal outcomes. This study investigated the roles of immune factors, systemic interactions, and neurobiological functions, including the Default Mode Network (DMN) and B-cell regulation, in PTSD remission and EMDR efficacy.</p><p><strong>Methodology: </strong>A systematic dry-lab analytical approach was employed using Artificial Neural Network (ANN) modeling and dynamic pathway analysis to reveal neurobiological factors, immune function, and neural network differences involved in PTSD remission and EMDR functionality. Publicly available gene expression datasets were analyzed, focusing on key pathways, interactions, and gene loci related to immune regulation, neurobiological modulation, and inflammatory response. Key molecules, including IL-6, IL-10, NPY, and OXY, were examined for potential contributions to neurobiological resilience, therapeutic response, and immune function in response to adverse stimuli.</p><p><strong>Results: </strong>The ANN analysis revealed that immunological function, notably through IL-10 and B-cell activity, plays a prominent role in PTSD remission, EMDR outcomes, and resilience. IL-10 emerged as central to B-cell differentiation and proliferation regarding trauma and resilience, indicating its potential as a biomarker for PTSD within the first year of symptom onset. Additional analyses implicated IL-6 and NPY as critical to longitudinal neurobiological resilience mechanisms. Interestingly, while OXY was initially classified as significant for social bonding and PTSD remission, analysis showed this gene only played a secondary mediating role, aligning with recent findings that OXY is not strictly necessary for prosocial outcomes such as PTSD remission.</p><p><strong>Conclusion: </strong>This study suggests that IL-10, IL-6, and NPY are key neuroimmune modulators in PTSD remission and may explain EMDR functionality. Immunologic activity may also explain variations in EMDR outcomes, such as therapeutic success, resistance, and relapse rates. These findings underscore the potential of targeted co-occurring immunotherapies, possible objective PTSD tests, potential objective measures for EMDR outcomes, and personalized approaches for enhanced treatment.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1751744"},"PeriodicalIF":3.3,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13500570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retraction: AD-Diff: enhancing Alzheimer's disease prediction accuracy through multimodal fusion.","authors":"","doi":"10.3389/fncom.2026.1957950","DOIUrl":"https://doi.org/10.3389/fncom.2026.1957950","url":null,"abstract":"<p><p>[This retracts the article DOI: 10.3389/fncom.2025.1484540.].</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1957950"},"PeriodicalIF":3.3,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13492123/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148790216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}