工程技术最新文献

The metacognition of one's planning strategy constitutes a "second-level" of metacognition that goes beyond the knowledge and monitoring of one's cognition and refers to the ability to use awareness mechanisms to regulate execution of present or future actions effectively. This study investigated the relation between metacognition of one's planning strategy and the behavioral and electrophysiological (EEG) correlates that support strategic planning abilities during performance in a complex decision-making task. Moreover, a possible link between task execution, metacognition, and individual differences (i.e., personality profiles and decision-making styles) was explored. A modified version of the Tower of Hanoi task was proposed to a sample of healthy participants, while their behavioral and EEG neurofunctional correlates of strategic planning were collected throughout the task with decisional valence. After the task, a metacognitive scale, the 10-item Big Five Inventory, the General Decision-Making Style inventory, and the Maximization Scale were administered. Results showed that the metacognitive scale enables to differentiate between the specific dimensions and levels of metacognition that are related to strategic planning behavioral performance and decision. Higher EEG delta power over left frontal cortex (AF7) during task execution positively correlates with the metacognition of one's planning strategy for the whole sample. While increased beta activity over the left frontal cortex (AF7) during task execution, higher metacognitive beliefs of efficacy and less willingness to change their strategy a posteriori were correlated with specific personality profiles and decision-making styles. These findings allow researchers to delve deeper into the multiple facets of metacognition of one's planning strategy in decision-making.

Insomnia, as a common sleep disorder, is the most common complaints in medical practice affecting a large proportion of the population on a situational, recurrent or chronic basis. It has been demonstrated that, during wakefulness, patients with insomnia exhibit increased EEG power in theta, beta, and gamma band. However, the relevant mechanisms underlying such power changes are still lack of understanding. In this paper, by combining the neural computational model with the real EEG data, we focus on exploring what's behind the EEG power changes for insomniac. We first develop a modified Liley model, named FSR-Liley, by respectively considering the fast and slow synaptic responses in inhibitory neurons along with the one-way projection between them. Then we introduce a parameter selection and evaluation method based on Markov chain Monte Carlo algorithm and Wasserstein distance, by which the sensitive parameters are selected automatically, and meanwhile, the optimal values of selected parameters are evaluated. Finally, through combining with EEG data, we determine the sensitive parameters in FSR-Liley and accordingly provide the mechanistic hypotheses: (1) decrease in $P_{e{i}_f}$ , corresponding to the input from the thalamus to cortical inhibitory population with fast synaptic response, leads to the increased theta and beta power; (2) decrease in $N_{e{i}_f}$ , corresponding to the projection from cortical excitatory population to inhibitory population with fast synaptic response, causes the increased gamma power. The results in this paper provide insights into the mechanisms of EEG power changes in insomnia and establish a theoretical foundation to support further experimental research.

Granule cells (GCs) are mainly responsible for receiving and integrating information from the entorhinal cortex and transferring it to the hippocampus to accomplish memory-related functions such as pattern separation. Owing to the heterogeneity of GCs, there are also two other subtypes, namely semilunar granule cells (SGCs) and hilar ectopic granule cells (HEGCs). In order to investigate their differences, here we examine the disparities in dendritic integration among the different subtypes of GCs. By utilizing biological experimental data, we developed detailed multi-compartment models for each type of GC. Our findings reveal that under the excitatory synaptic inputs (mediated by AMPA receptors), the dendritic integration of GCs, SGCs and HEGCs are linear, sublinear, and supralinear respectively. Furthermore, we propose that the sublinear integration observed in SGCs may be attributed to a high density of V-type potassium channels (K $_{\text{V}}^{}$ ) distributed in dendrites with smaller volume and higher input resistance; while the supralinear integration seen in HEGCs may be due to a high density of T-type calcium channels (Ca $_{\text{T}}^{}$ ) distributed in dendrites with larger volume and lower input resistance. Additionally, sodium channels, six types of potassium channels (K $_{\text{A}}^{}$ , K $_{\text{M}}^{}$ , sK $_{\text{DR}}^{}$ , fK $_{\text{DR}}^{}$ , BK, SK), and two types of calcium channels (Ca $_{\text{N}}^{}$ , Ca $_{\text{L}}^{}$ ) have minimal influence on their respective integration modes. We also found different integration modes exhibit varied somatic firing rates when subjected to different spatial synaptic activation sets, the HEGCs with the supralinear integration demonstrate higher somatic firing rates than the SGCs with the sublinear integration. These results provide theoretical insights into understanding the distinct roles played by these three subtypes of granule cells in memory-related functions within the dentate gyrus.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10226-0.

Genetically modified crop adoption in Canada has been the key driver in removing tillage as the lead form of weed control, due to increased weed control efficiency. Land use has transitioned from the use of summerfallow to continuous cropping, predominantly involving zero or minimum tillage practices. Prairie crop rotations have diversified away from mainly cereals to include three-year rotations of cereals, pulses, and oilseeds. Total herbicide volume applied has increased as crop production acres increased, but the rate of herbicide active ingredient applied per hectare has declined. Diverse crop rotations allow for weed control using herbicides with different modes of action, reducing selection pressure for resistant weed development. Herbicide-resistant weeds are an important concern for farmers, as the loss of key herbicides would make weed control exceedingly more difficult. The objective of this case study is to examine herbicide resistance weed development in the Canadian Prairies and to identify changes in resistance development following GM crop adoption.

The main aim of this study is to propose a novel convolutional neural network, named BrainNeXt, for the automated brain disorders detection using magnetic resonance images (MRI) images. Furthermore, we aim to investigate the performance of our proposed network on various medical applications. To achieve high/robust image classification performance, we gathered a new MRI dataset belonging to four classes: (1) Alzheimer's disease, (2) chronic ischemia, (3) multiple sclerosis, and (4) control. Inspired by ConvNeXt, we designed BrainNeXt as a lightweight classification model by incorporating the structural elements of the Swin Transformers Tiny model. By training our model on the collected dataset, a pretrained BrainNeXt model was obtained. Additionally, we have suggested a feature engineering (FE) approach based on the pretrained BrainNeXt, which extracted features from fixed-sized patches. To select the most discriminative/informative features, we employed the neighborhood component analysis selector in the feature selection phase. As the classifier for our patch-based FE approach, we utilized the support vector machine classifier. Our recommended BrainNeXt approach achieved an accuracy of 100% and 91.35% for training and validation. The recommended model obtained the test classification accuracy of 94.21%. To further improve the classification performance, we suggested a patch-based DFE approach, which achieved a test accuracy of 99.73%. The obtained results, surpassing 90% accuracy on the test dataset, demonstrate the effectiveness and high classification performance of the proposed models.

Fatigue, a complex and multifaceted symptom, profoundly influences quality of life, particularly among individuals suffering from chronic medical conditions or neurological disorders. This symptom not only exacerbates existing conditions but also hinders daily functioning, thereby perpetuating a vicious cycle of worsening symptoms and reduced physical activity. Given the pivotal role of the motor cortex (M1) in coordinating and executing voluntary movements, understanding how the cortex regulates fatigue is crucial. Despite its importance, the neural mechanisms underlying fatigue remain inadequately explored. In this study, we employed electrophysiological recordings in the M1 region of mice to investigate how excitation-inhibition dynamics and neural oscillations are regulated during exercise-induced fatigue. We observed that fatigue led to decreased voluntary physical activity and cognitive performance, manifesting as reduced running wheel distance, mean speed, exercise intensity, and exploratory behaviour. At the neural level, we detected increased firing frequencies for M1 neurons, including both pyramidal neurons and interneurons, along with heightened beta-band oscillatory activity and stronger coupling between beta-band oscillations and interneurons. These findings enhance our understanding of the mechanisms underlying fatigue, offering insights into behavioural, excitability, and oscillatory changes. The results of this study could pave the way for the development of novel intervention strategies to combat fatigue.

Pitch plays an essential role in music perception and forms the fundamental component of melodic interpretation. However, objectively detecting and decoding brain responses to musical pitch perception across subjects remains to be explored. In this study, we employed electroencephalography (EEG) as an objective measure to obtain the neural responses of musical pitch perception. The EEG signals from 34 subjects under hearing violin sounds at pitches G3 and B6 were collected with an efficient passive Go/No-Go paradigm. The lightweight modified EEGNet model was proposed for EEG-based pitch classification. Specifically, within-subject modeling with the modified EEGNet model was performed to construct individually optimized models. Subsequently, based on the within-subject model pool, a classifier ensemble (CE) method was adopted to construct the cross-subject model. Additionally, we analyzed the optimal time window of brain decoding for pitch perception in the EEG data and discussed the interpretability of these models. The experiment results show that the modified EEGNet model achieved an average classification accuracy of 77% for within-subject modeling, significantly outperforming other compared methods. Meanwhile, the proposed CE method achieved an average accuracy of 74% for cross-subject modeling, significantly exceeding the chance-level accuracy of 50%. Furthermore, we found that the optimal EEG data window for the pitch perception lies 0.4 to 0.9 s onset. These promising results demonstrate that the proposed methods can be effectively used in the objective assessment of pitch perception and have generalization ability in cross-subject modeling.

Neurodynamic observations indicate that the cerebral cortex evolved by self-organizing into functional networks, These networks, or distributed clusters of regions, display various degrees of attention maps based on input. Traditionally, the study of network self-organization relies predominantly on static data, overlooking temporal information in dynamic neuromorphic data. This paper proposes Temporal Self-Organizing (TSO) method for neuromorphic data processing using a spiking neural network. The TSO method incorporates information from multiple time steps into the selection strategy of the Best Matching Unit (BMU) neurons. It enables the coupled BMUs to radiate the weight across the same layer of neurons, ultimately forming a hierarchical self-organizing topographic map of concern. Additionally, we simulate real neuronal dynamics, introduce a glial cell-mediated Glial-LIF (Leaky Integrate-and-fire) model, and adjust multiple levels of BMUs to optimize the attention topological map.Experiments demonstrate that the proposed Self-organizing Glial Spiking Neural Network (SG-SNN) can generate attention topographies for dynamic event data from coarse to fine. A heuristic method based on cognitive science effectively guides the network's distribution of excitatory regions. Furthermore, the SG-SNN shows improved accuracy on three standard neuromorphic datasets: DVS128-Gesture, CIFAR10-DVS, and N-Caltech 101, with accuracy improvements of 0.3%, 2.4%, and 0.54% respectively. Notably, the recognition accuracy on the DVS128-Gesture dataset reaches 99.3%, achieving state-of-the-art (SOTA) performance.

In recent years, substantial strides have been made in the field of visual image reconstruction, particularly in its capacity to generate high-quality visual representations from human brain activity while considering semantic information. This advancement not only enables the recreation of visual content but also provides valuable insights into the intricate processes occurring within high-order functional brain regions, contributing to a deeper understanding of brain function. However, considering fusion semantics in reconstructing visual images from brain activity involves semantic-to-image guide reconstruction and may ignore underlying neural computational mechanisms, which does not represent true reconstruction from brain activity. In response to this limitation, our study introduces a novel approach that combines linear mapping with nonlinear noise suppression to reconstruct visual images perceived by subjects based on their brain activity patterns. The primary challenge associated with linear mapping lies in its susceptibility to noise interference. To address this issue, we leverage a flexible denoised deep convolutional neural network, which can suppress noise from linear mapping. Our investigation encompasses linear mapping as well as the training of shallow and deep autoencoder denoised neural networks, including a pre-trained, state-of-the-art denoised neural network. The outcome of our study reveals that combining linear image decoding with nonlinear noise reduction significantly enhances the quality of reconstructed images from human brain activity. This suggests that our methodology holds promise for decoding intricate perceptual experiences directly from brain activity patterns without semantic information. Moreover, the model has strong neural explanatory power because it shares structural and functional similarities with the visual brain.

A free calcium ion in the cytosol is essential for many physiological and physical functions. Also, it is known as a second messenger as the quantity of free calcium ions is an essential part of brain signaling. In this work, we have attempted to study calcium signaling in the presence of mitochondria, buffer, and endoplasmic reticulum fluxes. Small organelles called mitochondria are found in the nervous system and are involved in several cellular functions, including energy production, response to stress, calcium homeostasis regulation, and pathways leading to cell death. It has been discovered that buffer, endoplasmic reticulum, and mitochondria significantly affect calcium signaling. To investigate how various circumstances impact the quantity of calcium in the cytosol, a mathematical model of a second-order linear partial differential equation with fuzzy boundary conditions has been developed. Systems having ambiguous or imprecise boundary values can be effectively modeled and simulated with the help of fuzzy boundary conditions. Models can provide more dependable and instructive outcomes and become adaptable to real-world circumstances by implementing fuzzy logic into boundary conditions. In this paper, we observed the Fuzzy Laplace Transform to solve variable coefficient fuzzy differential equations using triangular fuzzy numbers. It is noted that maintaining the delicate calcium ion balance, which controls essential cellular functions, depends on the buffer affinity. Also, neurodegenerative illnesses like Alzheimer's, Parkinson's, etc. are linked to disruptions in the control of components such as buffers, mitochondria, and the endoplasmic reticulum.