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.

Neuron processes-axons and dendrites-have distinct branching patterns related to their biological function in the brain and body. Other non-neuronal cells in the nervous system, glia, also have characteristic branching morphologies. Our previous work has used biological scaling theory to connect branching patterns in neurons to biophysical function such as energy or conduction time minimization and material constrants in a compact, unifying mathematical model. Here, we use functionally relevant structural parameters related to asymmetric branching patterns extracted from our model as features in machine-learning classification methods to highlight differences between different types of neurons and glia as well as between healthy and diseased cells. Notably, we find that parameters related to information flow vary with position in the cell-that is, relative proximity of each branching junction to the soma (cell body) or synapses. We find that for some neuronal and glial cell type comparisons, such as comparisons between medium spiny neuron (MSN) dendrites, incorporating relative branching junction location significantly improves the performance of machine-learning classification methods. Our results imply that differences in information flow across cells drive specific morphological changes that correspond to localized regions of neuronal and glial cells. The promise of our methods and results lay foundation for future studies classifying neuronal and glial cells based on pathology, using our asymmetric scale factors and relative branching junction location as potential biomarkers to identify particular diseases based on both structural differences and the underlying differences in function.

This paper presents a comprehensive mechanistic model of a neuron with plasticity that explains how information input as time-varying signals is processed and stored. Additionally, the model addresses two long-standing, specific biological challenges: Integrating Hebbian and homeostatic plasticity, and identifying a concise synaptic learning rule. A biologically accurate small-signal equivalent-circuit model is derived through a one-to-one mapping from established ion-channel properties. The often-overlooked dynamics of the synaptic cleft is essential in this process. Analysis of the model reveals a simple and succinct learning rule, indicating that the neuron functions as an internal-feedback adaptive filter, a common concept in signal processing. Simulations confirm the model's functionality, stability, and convergence, demonstrating that even a single neuron without external feedback can act as a potent signal processor. The model replicates several key characteristics typical of biological neurons, which are seldom captured in other neuron models. It can encode time-varying functions, learn without risking instability, and bootstrap from a state where all synaptic weights are zero. This paper explores the function of neurons with a focus on biological accuracy, not computational efficiency. Unlike neuromorphic models, it does not aim to design devices. The electronic circuit analogy aids understanding by leveraging decades of electronics expertise but is not intended for physical implementation. This interdisciplinary work spans a broad range of subjects within the realm of neurobiophysics, including neurobiology, electronics, and signal processing.

Accurate determination of the progression phase of Alzheimer's disease (AD) is crucial for timely clinical decision-making, improved patient management, and personalized therapeutic interventions. However, reliably distinguishing between multiple disease stages using neuroimaging data remains a challenging task. This study proposes an advanced machine learning framework for multi-stage AD classification using magnetic resonance imaging (MRI) data. The architecture follows a two-tier design. In the first stage, convolutional neural networks (CNNs) are employed to extract deep and discriminative feature representations from MRI images. In the second stage, these features are classified using ensemble learning models, specifically XGBoost and LightGBM. Metaheuristic optimization strategies are applied to further enhance model performance. The proposed framework was evaluated using a publicly available Alzheimer's disease dataset under three different experimental configurations. Experimental results demonstrate that the proposed approach effectively addresses the multi-class classification problem across different AD progression stages. The optimized models achieved a maximum classification accuracy of 89.55%, indicating robust predictive performance and strong generalization capability. To improve transparency and clinical relevance, explainable artificial intelligence (XAI) techniques were incorporated to interpret model predictions and highlight feature importance. The results provide meaningful insights into neuroimaging biomarkers associated with AD progression and support the development of more interpretable and trustworthy diagnostic systems. Overall, the proposed framework contributes to improved data-driven decision support and offers a promising direction for future Alzheimer's disease diagnosis and staging research.

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.

Action potentials (AP) are the basic elements of information processing in the nervous system. Understanding AP generation mechanisms is a critical step to understand how neurons encode information. However, an individual neuron might fire APs with various shapes even in response to the same stimulus, and the mechanisms responsible for this variability remain unclear. Therefore, we analyzed four AP attributes including AP rapidity and threshold during consecutive bursts from three neuron types using intracellular electrophysiological recordings. In response to consecutive current steps, the AP attributes in evoked spike trains show two distinctive patterns across different neurons: (1) The first APs from each train always have comparable properties regardless of the stimulus strength; (2) The attributes of the subsequent APs during each pulse monotonically change during the burst, where the magnitude of AP attribute change during each pulse increases with increasing stimulation strength. Various conductance-based models were explored to determine if they replicated the observed AP bursts. The observed patterns could not be replicated using the classical HH-type models, or modified HH model with cooperative Na+ gating. However, adding ion concentration dynamics to the model reproduced the AP attribute variation, and the magnitude of change during a pulse correlated with change in dynamic reversal potential (DRP), but failed to replicate the first AP attributes pattern. Then, the role of cooperative Na+ gating on neuronal firing dynamics was investigated. Inclusion of cooperative gating restored the first APs’ attributes and enhanced the magnitude of modeled variation of some AP attributes to better agree with observed data. We conclude that changes in local ion concentrations could be responsible for the monotonic change in APs attributes during neuronal bursts, and cooperative gating of Na+ channels can enhance the effect. Thus, the two mechanisms could contribute to the observed variability in neuronal response.

Brain activities often follow an exponential family of distributions. The exponential distribution is the maximum entropy distribution of continuous random variables in the presence of a mean. The memoryless and peakless properties of an exponential distribution impose difficulties for data analysis methods. To estimate the rate parameter of multivariate exponential distribution from a time series of sensory inputs (i.e., observations), we constructed a hierarchical Bayesian inference model based on a variant of general hierarchical Brownian filter (GHBF). To account for the complex interactions among multivariate exponential random variables, the model estimates the second-order interaction of the rate intensity parameter in logarithmic space. Using variational Bayesian scheme, a family of closed-form and analytical update equations are introduced. These update equations also constitute a complete predictive coding framework. The simulation study shows that our model has the ability to evaluate the time-varying rate parameters and the underlying correlation structure of volatile multivariate exponentially distributed signals. The proposed hierarchical Bayesian inference model is of practical utility in analyzing high-dimensional neural activities.