Frontiers in Computational Neuroscience最新文献

{"title":"Further <i>N</i>-Frame networking dynamics of conscious observer-self agents via a functional contextual interface: predictive coding, double-slit quantum mechanical experiment, and decision-making fallacy modeling as applied to the measurement problem in humans and AI.","authors":"Darren J Edwards","doi":"10.3389/fncom.2025.1551960","DOIUrl":"https://doi.org/10.3389/fncom.2025.1551960","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Artificial intelligence (AI) has made some remarkable advances in recent years, particularly within the area of large language models (LLMs) that produce human-like conversational abilities via utilizing transformer-based architecture. These advancements have sparked growing calls to develop tests not only for intelligence but also for consciousness. However, existing benchmarks assess reasoning abilities across various domains but fail to directly address consciousness. To bridge this gap, this paper introduces the functional contextual &lt;i&gt;N&lt;/i&gt;-Frame model, a novel framework integrating predictive coding, quantum Bayesian (QBism), and evolutionary dynamics. This comprehensive model explicates how conscious observers, whether human or artificial, should update beliefs and interact within a quantum cognitive system. It provides a dynamic account of belief evolution through the interplay of internal observer states and external stimuli. By modeling decision-making fallacies such as the conjunction fallacy and conscious intent collapse experiments within this quantum probabilistic framework, the &lt;i&gt;N&lt;/i&gt;-Frame model establishes structural and functional equivalence between cognitive processes identified within these experiments and traditional quantum mechanics (QM). It is hypothesized that consciousness serves as an active participant in wavefunction collapse (or actualization of the physical definite states we see), bridging quantum potentiality and classical outcomes via internal observer states and contextual interactions via a self-referential loop. This framework formalizes decision-making processes within a Hilbert space, mapping cognitive states to quantum operators and contextual dependencies, and demonstrates structural and functional equivalence between cognitive and quantum systems in order to address the measurement problem. Furthermore, the model extends to testable predictions about AI consciousness by specifying informational boundaries, contextual parameters, and a conscious-time dimension derived from Anti-de Sitter/Conformal Field Theory correspondence (AdS/CFT). This paper theorizes that human cognitive biases reflect adaptive, evolutionarily stable strategies that optimize predictive accuracy (i.e., evolved quantum heuristic strategies rather than errors relative to classical rationality) under uncertainty within a quantum framework, challenging the classical interpretation of irrationality. The &lt;i&gt;N&lt;/i&gt;-Frame model offers a unified account of consciousness, decision-making, behavior, and quantum mechanics, incorporating the idea of finding truth without proof (thus overcoming Gödelian uncertainty), insights from quantum probability theory (such as the Linda cognitive bias findings), and the possibility that consciousness can cause waveform collapse (or perturbation) accounting for the measurement problem. It proposes a process for conscious time and branching worldlines to explain subjective experiences of time flow and ","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1551960"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11996842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144063045","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":"Prefrontal meta-control incorporating mental simulation enhances the adaptivity of reinforcement learning agents in dynamic environments.","authors":"JiHun Kim, Jee Hang Lee","doi":"10.3389/fncom.2025.1559915","DOIUrl":"https://doi.org/10.3389/fncom.2025.1559915","url":null,"abstract":"<p><strong>Introduction: </strong>Recent advances in computational neuroscience highlight the significance of prefrontal cortical meta-control mechanisms in facilitating flexible and adaptive human behavior. In addition, hippocampal function, particularly mental simulation capacity, proves essential in this adaptive process. Rooted from these neuroscientific insights, we present <i>Meta-Dyna</i>, a novel neuroscience-inspired reinforcement learning architecture that demonstrates rapid adaptation to environmental dynamics whilst managing variable goal states and state-transition uncertainties.</p><p><strong>Methods: </strong>This architectural framework implements prefrontal meta-control mechanisms integrated with hippocampal replay function, which in turn optimized task performance with limited experiences. We evaluated this approach through comprehensive experimental simulations across three distinct paradigms: the two-stage Markov decision task, which frequently serves in human learning and decision-making research; <i>stochastic GridWorldLoCA</i>, an established benchmark suite for model-based reinforcement learning; and a <i>stochastic Atari Pong</i> variant incorporating multiple goals under uncertainty.</p><p><strong>Results: </strong>Experimental results demonstrate <i>Meta-Dyna</i>'s superior performance compared with baseline reinforcement learning algorithms across multiple metrics: average reward, choice optimality, and a number of trials for success.</p><p><strong>Discussions: </strong>These findings advance our understanding of computational reinforcement learning whilst contributing to the development of brain-inspired learning agents capable of flexible, goal-directed behavior within dynamic environments.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1559915"},"PeriodicalIF":2.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11983510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143993343","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":"NeuroFusionNet: cross-modal modeling from brain activity to visual understanding.","authors":"Kehan Lang, Jianwei Fang, Guangyao Su","doi":"10.3389/fncom.2025.1545971","DOIUrl":"https://doi.org/10.3389/fncom.2025.1545971","url":null,"abstract":"<p><p>In recent years, the integration of machine vision and neuroscience has provided a new perspective for deeply understanding visual information. This paper proposes an innovative deep learning model, NeuroFusionNet, designed to enhance the understanding of visual information by integrating fMRI signals with image features. Specifically, images are processed by a visual model to extract region-of-interest (ROI) features and contextual information, which are then encoded through fully connected layers. The fMRI signals are passed through 1D convolutional layers to extract features, effectively preserving spatial information and improving computational efficiency. Subsequently, the fMRI features are embedded into a 3D voxel representation to capture the brain's activity patterns in both spatial and temporal dimensions. To accurately model the brain's response to visual stimuli, this paper introduces a Mutli-scale fMRI Timeformer module, which processes fMRI signals at different scales to extract both fine details and global responses. To further optimize the model's performance, we introduce a novel loss function called the fMRI-guided loss. Experimental results show that NeuroFusionNet effectively integrates image and brain activity information, providing more precise and richer visual representations for machine vision systems, with broad potential applications.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1545971"},"PeriodicalIF":2.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11978827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143990245","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":"AD-Diff: enhancing Alzheimer's disease prediction accuracy through multimodal fusion.","authors":"Lei Han","doi":"10.3389/fncom.2025.1484540","DOIUrl":"10.3389/fncom.2025.1484540","url":null,"abstract":"<p><p>Early prediction of Alzheimer's disease (AD) is crucial to improving patient quality of life and treatment outcomes. However, current predictive methods face challenges such as insufficient multimodal information integration and the high cost of PET image acquisition, which limit their effectiveness in practical applications. To address these issues, this paper proposes an innovative model, AD-Diff. This model significantly improves AD prediction accuracy by integrating PET images generated through a diffusion process with cognitive scale data and other modalities. Specifically, the AD-Diff model consists of two core components: the ADdiffusion module and the multimodal Mamba Classifier. The ADdiffusion module uses a 3D diffusion process to generate high-quality PET images, which are then fused with MRI images and tabular data to provide input for the Multimodal Mamba Classifier. Experimental results on the OASIS and ADNI datasets demonstrate that the AD-Diff model performs exceptionally well in both long-term and short-term AD prediction tasks, significantly improving prediction accuracy and reliability. These results highlight the significant advantages of the AD-Diff model in handling complex medical image data and multimodal information, providing an effective tool for the early diagnosis and personalized treatment of Alzheimer's disease.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1484540"},"PeriodicalIF":2.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11937103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718473","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":"Exploring the neural basis of creativity: EEG analysis of power spectrum and functional connectivity during creative tasks in school-aged children.","authors":"Gabriela Krumm, Vanessa Arán Filippetti, Magaly Catanzariti, Diego M Mateos","doi":"10.3389/fncom.2025.1548620","DOIUrl":"10.3389/fncom.2025.1548620","url":null,"abstract":"<p><p>Creativity is a fundamental aspect of human cognition, particularly during childhood. Exploring creativity through electroencephalography (EEG) provides valuable insights into the brain mechanisms underlying this vital cognitive process. This study analyzed the power spectrum and functional connectivity of interhemispheric and intrahemispheric brain activity during creative tasks in 15 Argentine children aged 9 to 12, using a 14-channel EEG system. The Torrance test of creative thinking (TTCT) was used, incorporating one figural and one verbal task. EEG metrics included relative power spectral density (rPSD) across Delta, Theta, Alpha, Beta, and Gamma bands. Spearman's Rho correlations were calculated between frequency bands and performance on creativity tasks, followed by functional connectivity assessment through coherence analysis across the [1-50] Hz spectrum. The results revealed significant increases in rPSD across all frequency bands during creative tasks compared to rest, with no significant differences between figural and verbal tasks. Correlational analysis revealed positive associations between the Beta band and the innovative and adaptive factors of the figural task. In contrast, for the verbal task, both the Beta and Gamma bands were positively related to flexibility, while the Alpha band showed a negative relationship with fluency and originality. Coherence analysis showed enhanced intrahemispheric synchronization, particularly in frontotemporal and temporo-occipital regions, alongside reduced interhemispheric frontal coherence. These findings suggest that creativity in children involves a dynamic reorganization of brain activity, characterized by oscillatory activation and region-specific connectivity changes. Our study contributes to a deeper understanding of the brain mechanisms supporting creativity during child development.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1548620"},"PeriodicalIF":2.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11937046/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718489","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":"Editorial: Computational intelligence for signal and image processing, volume II.","authors":"Deepika Koundal, Jussi Tohka","doi":"10.3389/fncom.2025.1581047","DOIUrl":"10.3389/fncom.2025.1581047","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1581047"},"PeriodicalIF":2.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11933044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143709280","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":"Editorial: Brain-inspired intelligence: the deep integration of brain science and artificial intelligence.","authors":"Ye Yuan, Xi Chen, Jian Liu","doi":"10.3389/fncom.2025.1553207","DOIUrl":"10.3389/fncom.2025.1553207","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1553207"},"PeriodicalIF":2.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656619","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":"Estimation of ionic currents and compensation mechanisms from recursive piecewise assimilation of electrophysiological data.","authors":"Stephen A Wells, Paul G Morris, Joseph D Taylor, Alain Nogaret","doi":"10.3389/fncom.2025.1458878","DOIUrl":"10.3389/fncom.2025.1458878","url":null,"abstract":"<p><p>The identification of ion channels expressed in neuronal function and neuronal dynamics is critical to understanding neurological disease. This program calls for advanced parameter estimation methods that infer ion channel properties from the electrical oscillations they induce across the cell membrane. Characterization of the expressed ion channels would allow detecting channelopathies and help devise more effective therapies for neurological and cardiac disease. Here, we describe Recursive Piecewise Data Assimilation (RPDA), as a computational method that successfully deconvolutes the ionic current waveforms of a hippocampal neuron from the assimilation of current-clamp recordings. The strength of this approach is to simultaneously estimate all ionic currents in the cell from a small but high-quality dataset. RPDA allows us to quantify collateral alterations in non-targeted ion channels that demonstrate the potential of the method as a drug toxicity counter-screen. The method is validated by estimating the selectivity and potency of known ion channel inhibitors in agreement with the standard pharmacological assay of inhibitor potency (IC50).</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1458878"},"PeriodicalIF":2.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11913807/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656547","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":"A new method for identifying and evaluating depressive disorders in young people based on cognitive neurocomputing: an exploratory study.","authors":"Jiakang Liu, Kai Li, Shuwu Li, Shangjun Liu, Chen Wang, Shouqiang Huang, Yuting Tu, Bo Wang, Pengpeng Zhang, Yuntian Luo, Guanqun Sun, Tong Chen","doi":"10.3389/fncom.2025.1555416","DOIUrl":"10.3389/fncom.2025.1555416","url":null,"abstract":"<p><strong>Background: </strong>Depressive disorders are one of the most common mental disorders among young people. However, there is still a lack of objective means to identify and evaluate young people with depressive disorders quickly. Cognitive impairment is one of the core characteristics of depressive disorders, which is of great value in the identification and evaluation of young people with depressive disorders.</p><p><strong>Methods: </strong>This study proposes a new method for identifying and evaluating depressive disorders in young people based on cognitive neurocomputing. The method evaluates cognitive impairments such as reduced attention, executive dysfunction, and slowed information processing speed that may exist in the youth depressive disorder population through an independently designed digital evaluation paradigm. It also mines digital biomarkers that can effectively identify these cognitive impairments. A total of 50 young patients with depressive disorders and 47 healthy controls were included in this study to validate the method's identification and evaluation capability.</p><p><strong>Results: </strong>The differences analysis results showed that the digital biomarkers of cognitive function on attention, executive function, and information processing speed extracted in this study were significantly different between young depressive disorder patients and healthy controls. Through stepwise regression analysis, four digital biomarkers of cognitive function were finally screened. The area under the curve for them to jointly distinguish patients with depressive disorders from healthy controls was 0.927.</p><p><strong>Conclusion: </strong>This new method rapidly characterizes and quantifies cognitive impairment in young people with depressive disorders. It provides a new way for organizations, such as schools, to quickly identify and evaluate the population of young people with depressive disorders based on human-computer interaction.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1555416"},"PeriodicalIF":2.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604331","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":"Time-domain brain: temporal mechanisms for brain functions using time-delay nets, holographic processes, radio communications, and emergent oscillatory sequences.","authors":"Janet M Baker, Peter Cariani","doi":"10.3389/fncom.2025.1540532","DOIUrl":"10.3389/fncom.2025.1540532","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Time is essential for understanding the brain. A temporal theory for realizing major brain functions (e.g., sensation, cognition, motivation, attention, memory, learning, and motor action) is proposed that uses temporal codes, time-domain neural networks, correlation-based binding processes and signal dynamics. It adopts a signal-centric perspective in which neural assemblies produce circulating and propagating characteristic temporally patterned signals for each attribute (feature). Temporal precision is essential for temporal coding and processing. The characteristic spike patterns that constitute the signals enable general-purpose, multimodal, multidimensional vectorial representations of objects, events, situations, and procedures. Signals are broadcast and interact with each other in spreading activation time-delay networks to mutually reinforce, compete, and create new composite patterns. Sequences of events are directly encoded in the relative timings of event onsets. New temporal patterns are created through nonlinear multiplicative and thresholding signal interactions, such as mixing operations found in radio communications systems and wave interference patterns. The newly created patterns then become markers for bindings of specific combinations of signals and attributes (e.g., perceptual symbols, semantic pointers, and tags for cognitive nodes). Correlation operations enable both bottom-up productions of new composite signals and top-down recovery of constituent signals. Memory operates using the same principles: nonlocal, distributed, temporally coded memory traces, signal interactions and amplifications, and content-addressable access and retrieval. A short-term temporary store is based on circulating temporal spike patterns in reverberatory, spike-timing-facilitated circuits. A long-term store is based on synaptic modifications and neural resonances that select specific delay-paths to produce temporally patterned signals. Holographic principles of nonlocal representation, storage, and retrieval can be applied to temporal patterns as well as spatial patterns. These can automatically generate pattern recognition (wavefront reconstruction) capabilities, ranging from objects to concepts, for distributed associative memory applications. The evolution of proposed neural implementations of holograph-like signal processing and associative content-addressable memory mechanisms is discussed. These can be based on temporal correlations, convolutions, simple linear and nonlinear operations, wave interference patterns, and oscillatory interactions. The proposed mechanisms preserve high resolution temporal, phase, and amplitude information. These are essential for establishing high phase coherency and determining phase relationships, for binding/coupling, synchronization, and other operations. Interacting waves can sum constructively for amplification, or destructively, for suppression, or partially. Temporal precision, phase-locking, phase","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1540532"},"PeriodicalIF":2.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11877394/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556248","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}