Frontiers in Computational Neuroscience最新文献

Childhood absence epilepsy (CAE) often resolves during adolescence, a period marked by hormonal and neurosteroid changes associated with puberty. However, remission does not occur in all individuals. To investigate this clinical heterogeneity, we developed a simplified thalamocortical model with a layered cortical structure, using deep-layer intrinsically bursting (IB) neurons to represent frontal cortex and regular spiking (RS) neurons modeling the parietal cortex. By simulating two cortical configurations, we explored how variations in neuronal composition and frontocortical connectivity influence seizure dynamics and the effectiveness of allopregnanolone (ALLO) in resolving pathological spike-wave discharges (SWDs) associated with CAE. While both models exhibited similar physiological and pathological oscillations, only the parietal-dominant network (with a higher proportion of RS neurons in layer 5) recovered from SWDs under increased frontocortical connectivity following ALLO administration. These findings suggest that neuronal composition critically modulates ALLO-mediated resolution of SWDs, providing a mechanistic link between structural connectivity and clinical outcomes in CAE, and highlighting the potential for personalized treatment strategies based on underlying network architecture.

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.

Introduction: Osteoporosis is the leading cause of sudden bone fractures. This is a silent and deadly disease that can affect any part of the body, such as the spine, hips, and knee bones.

Aim: To measure bone mineral density, dual-energy X-ray absorptiometry (DXA) scans help radiologists and other medical professionals identify early signs of osteoporosis in the spine.

Methods: A proposed 21-layer convolutional neural network (CNN) model is implemented and validated to automatically detect osteoporosis in spine DXA images. The dataset contains 174 spine DXA images, including 114 affected by osteoporosis and the rest normal or non-fractured. To improve training, the dataset is expanded using various data augmentation techniques.

Results: The classification performance of the proposed model is compared with that of four popular pre-trained models: ResNet-50, Visual Geometry Group 16 (VGG-16), VGG-19, and InceptionV3. With an F1-score of 97.16%, recall of 95.41%, classification accuracy of 97.14%, and precision of 99.04%, the proposed model consistently outperforms competing approaches.

Conclusion: The proposed paradigm would therefore be very valuable to radiologists and other medical professionals. The proposed approach's capacity to detect, monitor, and diagnose osteoporosis may reduce the risk of developing the condition.

The spatiotemporal learning rule (STLR) can reproduce synaptic plasticity in the hippocampus. Analysis of the synaptic weights in the network with the STLR is challenging. Consequently, our previous research only focused on the network's outputs. However, a detailed analysis of the STLR requires focusing on the synaptic weights themselves. To address this issue, we mapped the synaptic weights to a distance space and analyzed the characteristics of the STLR. The results indicate that the synaptic weights form a fractal-like structure in Euclidean distance space. Furthermore, three analytical approaches-multi-dimensional scaling, estimating fractal dimension, and modeling with an iterated function system-demonstrate that the STLR forms a fractal structure in the synaptic weights through fractal coding. These findings contribute to clarifying the learning mechanisms in the hippocampus.

Robustness, defined as a system's ability to maintain functional reliability in the face of perturbations, is achieved through its capacity to filter external disturbances using internal priors encoded in its structure and states. While biophysical neural networks are widely recognized for their robustness, the precise mechanisms underlying this resilience remain poorly understood. In this study, we explore how orientation-selective neurons arranged in a one-dimensional ring network respond to perturbations, with the aim of uncovering insights into the robustness of visual subsystems in the brain. By analyzing the steady-state dynamics of a rate-based network, we characterize how the activation state of neurons influences the network's response to disturbances. Our results demonstrate that the activation state of neurons, rather than their firing rates alone, governs the network's sensitivity to perturbations. We further show that lateral connectivity modulates this effect by shaping the response profile across spatial frequency components. These findings suggest a state-dependent filtering mechanism that contributes to the robustness of visual circuits, offering theoretical insight into how different components of perturbations are selectively modulated within the network.

Introduction: Accurate and early identification of brain tumors is essential for improving therapeutic planning and clinical outcomes. Manual segmentation of Magnetic Resonance Imaging (MRI) remains time-consuming and subject to inter-observer variability. Computational models that combine Artificial Intelligence and biomedical imaging offer a pathway toward objective and efficient tumor delineation. The present study introduces a deep learning framework designed to enhance brain tumor segmentation performance.

Methods: A comprehensive ensemble architecture was developed by integrating Generative Autoencoders with Attention Mechanisms (GAME), Convolutional Neural Networks, and attention-augmented U-Net segmentation modules. The dataset comprised 5,880 MRI images sourced from the BraTS 2023 benchmark distribution accessed via Kaggle, partitioned into training, validation, and testing subsets. Preprocessing included intensity normalization, augmentation, and unsupervised feature extraction. Tumor segmentation employed an attention-based U-Net, while tumor classification utilized a CNN coupled with Transformer-style self-attention. The Generative Autoencoder performed unsupervised representation learning to refine feature separability and enhance robustness to MRI variability.

Results: The proposed framework achieved notable performance improvements across multiple evaluation metrics. The segmentation module produced a Dice Coefficient of 0.85 and a Jaccard Index of 0.78. The classification component yielded an accuracy of 87.18 percent, sensitivity of 88.3 percent, specificity of 86.5 percent, and an AUC-ROC of 0.91. The combined use of generative modeling, attention mechanisms, and ensemble learning improved tumor localization, boundary delineation, and false positive suppression compared with conventional architectures.

Discussion: The findings indicate that enriched representation learning and attention-driven feature refinement substantially elevate segmentation accuracy on heterogeneous MRI data. The integration of unsupervised learning within the pipeline supported improved generalization across variable imaging conditions. The demonstrated performance suggests strong potential for clinical utility, although broader validation across external datasets is recommended to further substantiate generalizability.

In learning goal-directed behavior, state representation is important for adapting to the environment and achieving goals. A predictive state representation called successive representation (SR) has recently attracted attention as a candidate for state representation in animal brains, especially in the hippocampus. The relationship between the SR and the animal brain has been studied, and several neural network models for computing the SR have been proposed based on the findings. However, studies on implementation of the SR involving action selection have not yet advanced significantly. Therefore, we explore possible mechanisms by which the SR is utilized biologically for action selection and learning optimal action policies. The actor-critic architecture is a promising model of animal behavioral learning in terms of its correspondence to the anatomy and function of the basal ganglia, so it is suitable for our purpose. In this study, we construct neural network models for behavioral learning using the SR. By using them to perform reinforcement learning, we investigate their properties. Specifically, we investigated the effect of using different state representations for the actor and critic in the actor-critic method, and also compared the actor-critic method with Q-learning and SARSA. We found the difference between the effect of using the SR for the actor and the effect of using the SR for the critic in the actor-critic method, and observed that using the SR in conjunction with one-hot encoding makes it possible to learn with the benefits of both representations. These results suggest the possibility that the striatum can learn using multiple state representations complementarily.

In algebraic topology, a k-dimensional simplex is defined as a convex polytope consisting of k + 1 vertices. If spatial dimensionality is not considered, it corresponds to the complete graph with k + 1 vertices in graph theory. The alternating sum of the number of simplices across dimensions yields a topological invariant known as the Euler characteristic, which has gained significant attention due to its widespread application in fields such as topology, homology theory, complex systems, and biology. The most common method for calculating the Euler characteristic is through simplicial decomposition and the Euler-Poincaré formula. In this study, we introduce a new "subgraph" polynomial, termed the simplex polynomial, and explore some of its properties. Using those properties, we provide a new method for computing the Euler characteristic and prove the existence of the Euler characteristic as an arbitrary integer by constructing the corresponding simplicial complex structure. When the Euler characteristic is 1, we determined a class of corresponding simplicial complex structures. Moreover, for three common network structures, we present the recurrence relations for their simplex polynomials and their corresponding Euler characteristics. Finally, at the end of this study, three basic questions are raised for the interested readers to study deeply.