Frontiers in Computational Neuroscience最新文献

{"title":"A critical analysis of MBTI-based personality profiling with large language models.","authors":"Jean Marie Tshimula, René Manassé Galekwa, Belkacem Chikhaoui","doi":"10.3389/fncom.2026.1800284","DOIUrl":"https://doi.org/10.3389/fncom.2026.1800284","url":null,"abstract":"<p><p>This paper critically analyzes MBTI-based personality profiling using Large Language Models (LLMs), examining both their use as tools for inferring human personality and as subjects evaluated through psychometric frameworks. We review recent work (2020-2025) spanning traditional machine learning, fine-tuned transformer models, and zero-shot prompting approaches across datasets such as Kaggle MBTI, PersonalityCafe, Pandora, and MBTIBench. While top-performing LLM-based systems report 75%-85% accuracy at the dichotomy level, improvements over baselines are often modest, domain-dependent, and sensitive to dataset biases. Recent benchmarks employing soft labels reveal systematic issues, including polarized predictions, overconfidence, and limited calibration relative to population trait distributions. Beyond predictive performance, we examine emerging research that applies MBTI instruments directly to LLMs, showing that models exhibit reproducible yet context-dependent \"personality-like\" profiles, often skewed toward socially desirable traits due to alignment training. These findings raise conceptual questions about whether stable internal dispositions can meaningfully be attributed to generative systems whose outputs vary across prompts and versions. We argue that MBTI-based modeling with LLMs faces three core challenges: psychometric limitations of the MBTI construct itself, methodological weaknesses in self-reported training data, and philosophical ambiguity regarding the notion of AI personality. The paper concludes by outlining ethical risks, evaluation gaps, and research directions for more rigorous, calibrated, and theoretically grounded personality modeling in artificial intelligence systems.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1800284"},"PeriodicalIF":2.3,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13143990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147835479","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":"Deterministic, stochastic, and mean-field PDE models in neuroscience.","authors":"Coşkun Çetin, Jose Roberto Castilho Piqueira, Burhaneddin İzgi, Ayse Peker-Dobie, Semra Ahmetolan, Murat Özkaya","doi":"10.3389/fncom.2026.1762692","DOIUrl":"https://doi.org/10.3389/fncom.2026.1762692","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1762692"},"PeriodicalIF":2.3,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13136274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147835509","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":"Commentary: Editorial: The convergence of AI, LLMs, and industry 4.0: enhancing BCI, HMI, and neuroscience research.","authors":"Alessandro Rossi","doi":"10.3389/fncom.2026.1810869","DOIUrl":"https://doi.org/10.3389/fncom.2026.1810869","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1810869"},"PeriodicalIF":2.3,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13111400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147766974","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":"Adaptation modulates effective connectivity and network stability.","authors":"Thomas J Richner, Martynas Dervinis, Brian Nils Lundstrom","doi":"10.3389/fncom.2026.1761735","DOIUrl":"https://doi.org/10.3389/fncom.2026.1761735","url":null,"abstract":"<p><p>The brain is a highly recurrent, nonlinear network hypothesized to remain near the edge of chaos for optimal performance. Excitation and inhibition must be balanced precisely within every neuron to ensure a consistent level of dynamical stability and rich dynamics during transition to chaos. However, analysis of biologically realistic synaptic weight matrices suggests that sparsity and low-dimensional structure interact such that there is no known synaptic balancing rule that constrains the stability (i.e., eigenvalues) of the network while also preserving computationally useful, low-dimensional structure. Further, even if a network were well-balanced, external stimuli interact with the nonlinear activation functions to unbalance the network in real time. Therefore, the brain must utilize dynamic, rather than static, mechanisms to actively regulate its level of stability. We propose that two specific adaptation mechanisms, spike frequency adaptation (SFA) and short-term synaptic depression (STD), continuously modulate the effective connectivity, keeping the brain near the edge of chaos and reducing dynamical fluctuations caused by stimuli. This theoretical framework links intrinsic and synaptic negative feedback mechanisms to network-level dynamics. This offers an explanation of why data-driven modeling of human brain signals, an exciting and useful method in epilepsy and anesthesiology research, seems to require linear time-varying (LTV) models which are refit every half second: difficult to observe adaptation processes interact with nonlinearities to make connectivity effectively dynamic at the macroelectrode scale. We suggest that compromised adaptation may underlie neurological conditions characterized by altered excitability, and that targeted brain stimulation could be used to probe the regulatory action of adaptation.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1761735"},"PeriodicalIF":2.3,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13106433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147766981","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: Advancements in neural coding: sensory perception and multiplexed encoding strategies.","authors":"Mohammad Amin Kamaleddin","doi":"10.3389/fncom.2026.1834521","DOIUrl":"https://doi.org/10.3389/fncom.2026.1834521","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1834521"},"PeriodicalIF":2.3,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13099749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147766971","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":"Simulated target search by bats using biomimetic SCAT biosonar model.","authors":"James A Simmons, Prithvi Thakur, Ashok Ragavendran, Chen Ming, Andrea Megela Simmons","doi":"10.3389/fncom.2026.1805106","DOIUrl":"https://doi.org/10.3389/fncom.2026.1805106","url":null,"abstract":"<p><p>Echolocating big brown bats broadcast short, wideband ultrasonic FM pulses for foraging and navigation. These broadcasts contain frequencies from 100 to 20 kHz (wavelengths 0.34-1.7 cm). Bats perceive target distance by measuring the time delay between the outgoing pulse and the returning echo. Acuity of this delay perception depends on the frequency content of echoes and the associated microsecond-level coherence between neural representations of the 1st and 2nd harmonic frequencies. Bats perceive target shape by estimating differences in the delay of mini-echoes from different reflecting points, or glints, within the target. A matched-filter receiver would register glints as prominent peaks in the pulse-echo cross-correlation output, but in bats the overlapping glint reflections mix together to create echo interference patterns that are transposed back into delay estimates. The process is modeled as spectrogram correlation and transformation (SCAT). The first, nearest glint is registered by echo delay itself, but subsequent glints are extracted from the nulls in the interference spectrum. Here, the SCAT receiver was evaluated for its ability to locate targets with a specific glint spacing in the 2D range/cross-range plane while rejecting other targets with larger or smaller spacings.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1805106"},"PeriodicalIF":2.3,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13099864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147767037","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":"Replication challenges in linking personality to resting-state functional connectomics.","authors":"Nikola Jajcay, David Tomeček, Iveta Fajnerová, Jan Rydlo, Jaroslav Tintěra, Jiří Horáček, Jiří Lukavský, Jaroslav Hlinka","doi":"10.3389/fncom.2026.1796308","DOIUrl":"https://doi.org/10.3389/fncom.2026.1796308","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1796308"},"PeriodicalIF":2.3,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13096096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147766997","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":"Designing implicit population learners: a permutation-equivariant state space approach for brain disease diagnosis.","authors":"Chuan Yang","doi":"10.3389/fncom.2026.1780552","DOIUrl":"https://doi.org/10.3389/fncom.2026.1780552","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 <i>O</i>(<i>N</i>) memory scaling without the quadratic computational bottlenecks typical of graph-based attention networks.</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1780552"},"PeriodicalIF":2.3,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13066267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147671887","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: Passive brain-computer interfaces: moving from lab to real-world application.","authors":"Vincenzo Ronca, Luca Longo, Rossella Capotorto, Pietro Aricò","doi":"10.3389/fncom.2026.1826791","DOIUrl":"https://doi.org/10.3389/fncom.2026.1826791","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1826791"},"PeriodicalIF":2.3,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13056873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147644745","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":"NeuralVisionNet: a probabilistic neural process model for continuous visual anticipation.","authors":"Han He, Ruinan Chen, Yixiang Wang, Xia Chen","doi":"10.3389/fncom.2026.1781080","DOIUrl":"https://doi.org/10.3389/fncom.2026.1781080","url":null,"abstract":"<p><p>The ability to anticipate future events continuously is a hallmark of biological vision, yet standard deep learning models often struggle with long-term coherence due to the rigid discretization of time. In this paper, we propose NeuralVisionNet, a probabilistic framework that models visual anticipation as a continuous generative process, drawing inspiration from the predictive coding mechanisms of the hippocampal-entorhinal circuit. Our architecture synergizes hierarchical Video Swin Transformers with Attentive Neural Processes, employing a novel grid-like coding scheme to represent spatiotemporal dynamics as a continuous function rather than a fixed sequence of frames. Furthermore, we introduce a variational global latent variable to encode the \"event gist,\" ensuring semantic consistency over extended horizons. Extensive evaluations on KTH, Human 3.6M, and UCF 101 benchmarks demonstrate that NeuralVisionNet significantly outperforms state-of-the-art stochastic baselines in perceptual quality (FVD) and structural fidelity (SSIM), offering a robust computational proof-of-concept for continuous, bio-inspired visual forecasting.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1781080"},"PeriodicalIF":2.3,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13067874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147671867","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}