Cognitive Neurodynamics最新文献

{"title":"Effects of dendritic properties on the correlations in ionic channels emerging from firing rate homeostasis: a two-compartment modeling study.","authors":"Guosheng Yi, Jiayi Cui, Ruifeng Bai","doi":"10.1007/s11571-025-10297-z","DOIUrl":"10.1007/s11571-025-10297-z","url":null,"abstract":"<p><p>Homeostatic regulation of firing rate is an important feature of neural excitability, which is achieved through feedback control of diverse ionic channel expression levels. The output firing rate is controlled by the active currents and passive properties of the dendrites. The objective of this study is to determine how dendritic properties affect the homeostatic regulation of somatic firing rate. We used a two-compartment Pinsky-Rinzel model to simulate action potentials in a pyramidal neuron in response to external inputs. We applied a feedback framework to determine the maximum ionic conductances during homeostatic regulation and examined the pairwise correlations among these conductances. We find that the effective regulation of somatic firing rate could be achieved through controlling both somatic and dendritic ionic channels. The correlations among these channels are lower than those emerging from the regulation through the control of somatic or dendritic channels. It is also shown that increasing the number of adjustable channels alters ionic channel correlations when the additional channel has a strong compensatory relationship with other channels. Compared to the coupling conductance between two compartments, varying the proportion of area occupied by the dendrite produces a greater effect on firing rate dynamics and expression correlations between adjustable channels in both dendrite and soma. The results reveal that dendritic ionic channels, morphological feature and dendritic-somatic coupling are all factors that influence the correlations in ionic channel expression. These findings provide a biophysical basis for the relationship between dendritic properties and neuronal information processing.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10297-z.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"107"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209171/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of deep learning for multi-scale behavioral analysis in SNCA E46K Parkinson's disease drosophila.","authors":"Keyi Dong, April Burch, Kang Huang","doi":"10.1007/s11571-025-10294-2","DOIUrl":"10.1007/s11571-025-10294-2","url":null,"abstract":"<p><p><i>Drosophila melanogaster</i> is widely used as a model organism in Parkinson's disease research. However, due to the complexity of motion capture and the challenges of quantitatively assessing spontaneous behavior in <i>Drosophila melanogaster</i>, it remains technically difficult to identify symptoms of Parkinson's disease within <i>Drosophila</i> based on objective spontaneous behavioral characteristics. Here, we present an automated multi-scale behavioral phenotyping pipeline that classifies phenotypes related to Parkinson's disease using motion features extracted from pose estimation data of wild-type and Synuclein Alpha E46K mutant <i>Drosophila melanogaster</i>. Locomotor activity was recorded in a custom-designed 3D-printed behavioral trap, and body kinematics were analyzed using a markerless pose estimation tool to extract numerical features such as movement speed, tremor-like oscillations, and limb motion patterns. Beyond kinematic analysis, we applied unsupervised clustering to the pose-derived trajectories to extract recurrent movement subtypes that characterize spontaneous behavioral sequences. We found that kinematic features alone were insufficient to distinguish mutant flies from normal individuals, whereas behavioral sequence patterns captured through unsupervised clustering enabled robust group separation. Combining both feature types further enhanced classification accuracy, with the best model achieving 85%. This system provides an objective and scalable approach for analyzing behavior related to Parkinson's disease in <i>Drosophila melanogaster</i>, with potential applications in monitoring disease progression and screening pharmaceutical compounds.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"105"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reward masks the learning of cognitive control demand.","authors":"Bettina Bustos, Jiefeng Jiang, Wouter Kool","doi":"10.1007/s11571-025-10307-0","DOIUrl":"https://doi.org/10.1007/s11571-025-10307-0","url":null,"abstract":"<p><p>Cognitive control refers to a set of cognitive functions that modulate other cognitive processes to align with internal goals. Recent research has shown that cognitive control can flexibly adapt to internal and external factors such as reward, effort, and environmental demands. This suggests that learning processes track changes in these factors and drive an optimization process to determine how cognitive control should be applied in changing situations. In real life, multiple factors often simultaneously affect how cognitive control is deployed. However, previous studies mainly concern how cognitive control adjusts to changes in a single factor. Here, we investigate how cognitive control learns to adjust to two concurrently changing factors: statistical regularity in cognitive control demand and performance-contingent reward. We consider two competing hypotheses: reward promotes cognitive control to adjust to cognitive control demand, and the processing of reward information obstructs the adaptation to cognitive control demand. In our experiment, statistical regularity in cognitive control demand is manipulated within subjects such that some stimuli require higher levels of cognitive control than others. Reward is manipulated across subjects. Using a computational model that captures temporal changes in cognitive control, we find that in the absence of reward, participants can adjust to different levels of cognitive control demand. Importantly, when performance-contingent reward is available, participants fail to adapt to changes in cognitive control demand. The findings support the hypothesis that reward blocks the learning of cognitive control.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10307-0.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"114"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12274171/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HWI encoding/decoding of a non-invasive HWI-BCI paradigm based on temporal variation abundance scale.","authors":"Peng Ding, Fan Wang, Lei Zhao, Anming Gong, Yunfa Fu","doi":"10.1007/s11571-025-10274-6","DOIUrl":"10.1007/s11571-025-10274-6","url":null,"abstract":"<p><p>The performance of non-invasive Handwriting Imagery (HWI) input in Brain-computer interface (BCI) systems is highly dependent on the paradigms employed, yet there is limited research on interpretable scales to measure how HWI-BCI paradigms and neural encoding designs affect performance. This study introduces the \"Temporal Variation Abundance\" metric and utilizes it to design two classes of handwriting imagery paradigms: Low Temporal Variation Abundance (LTVA) and High Temporal Variation Abundance (HTVA). A dynamic time warping algorithm based on random templates (rt-DTW) is proposed to align HWI velocity fluctuations using EEG. Comprehensive comparisons of these experimental paradigms are conducted in terms of feature space distance, offline and online classification accuracy, and cognitive load assessment using functional near-infrared spectroscopy. Results indicate that HTVA-HWI exhibits lower velocity stability but demonstrates higher spatial distance, offline classification accuracy, online testing classification accuracy, and lower cognitive load. This study provides deep insights into paradigm design for non-invasive HWI-BCI and scales of neural encoding, offering new theoretical support and methodological insights for future advancements in brain-computer interaction.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"130"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a transparent and interpretable AI model for medical image classifications.","authors":"Binbin Wen, Yihang Wu, Tareef Daqqaq, Ahmad Chaddad","doi":"10.1007/s11571-025-10343-w","DOIUrl":"https://doi.org/10.1007/s11571-025-10343-w","url":null,"abstract":"<p><p>The integration of artificial intelligence (AI) into medicine is remarkable, offering advanced diagnostic and therapeutic possibilities. However, the inherent opacity of complex AI models presents significant challenges to their clinical practicality. This paper focuses primarily on investigating the application of explainable artificial intelligence (XAI) methods, with the aim of making AI decisions transparent and interpretable. Our research focuses on implementing simulations using various medical datasets to elucidate the internal workings of the XAI model. These dataset-driven simulations demonstrate how XAI effectively interprets AI predictions, thus improving the decision-making process for healthcare professionals. In addition to a survey of the main XAI methods and simulations, ongoing challenges in the XAI field are discussed. The study highlights the need for the continuous development and exploration of XAI, particularly from the perspective of diverse medical datasets, to promote its adoption and effectiveness in the healthcare domain. Our code is available at https://github.com/AIPMLab/XAI_-review-2024.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"149"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12450194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electroencephalography evidence of functional connectivity modulation and its correlation with bimanual visuomotor learning.","authors":"Chatrin Phunruangsakao, Chihiro Hosoda, Mitsuhiro Hayashibe","doi":"10.1007/s11571-025-10336-9","DOIUrl":"10.1007/s11571-025-10336-9","url":null,"abstract":"<p><p>Recent studies have shown that neuroplasticity related to sensorimotor adaptation can occur within short time frames, ranging from minutes to hours. However, it remains unclear whether bimanual training can induce similar effects. Therefore, the objective is to investigate immediate functional brain changes following brief bimanual visuomotor adaptation training. Node and edge-level electroencephalogram functional connectivity analysis and principal component regression were employed to examine changes related to visuomotor tracking task performance. The results revealed significant post-training improvements in bimanual performance, along with decreased node closeness centrality in the non-dominant right frontal and sensorimotor regions within the beta band, as well as in the right frontal, sensorimotor, and occipital regions within the gamma band. Edge-wise analysis indicated reduced beta- and gamma-band connectivity in the right hemisphere, aligning with the node-wise findings. Additionally, theta-band closeness centrality in the frontal, centroparietal, occipital, and temporal regions was positively correlated with bimanual performance, indicating a shift toward more centralized processing as performance increased. Principal component regression further demonstrated its predictive value for bimanual visuomotor performance. This study demonstrates that brief bimanual training elicits immediate functional connectivity changes associated with improved motor performance, particularly reduced right hemisphere beta/gamma connectivity and increased theta centrality. These findings highlight dynamic neural reorganization during bimanual adaptation. However, the interpretation of the results is limited by small sample size, EEG's low spatial resolution, and bias in functional connectivity estimation. These findings provide insights into adaptation mechanisms that could inform rehabilitation strategies for individuals with motor impairments.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"152"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12460210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"R-CNN-TPOT: a new hybrid machine learning network for brain age prediction using EEG signal.","authors":"Sundas Almas, Pedro Antonio Valdes Sosa, Rana Muhammad Ali Washakh, Rana Muhammad Umar Waque","doi":"10.1007/s11571-025-10339-6","DOIUrl":"https://doi.org/10.1007/s11571-025-10339-6","url":null,"abstract":"<p><p>Brain age refers to the significant changes in electroencephalogram (EEG) signals that occur as people age. The chronological age can be compared to the brain age to determine the variations from the normal ageing process. With the rise of Machine Learning (ML), many brain age prediction methods have been developed using brain imaging. However, EEG-based approaches remain underexplored and have not utilized the Tree-based Pipeline Optimization Tool (TPOT). To subdue this problem, a novel hybrid ML technique is proposed for predicting brain age from EEG signals. The proposed method uses different features, such as spectral features, statistical features, frequency domain features and decomposition domain features. Additionally, a new ML approach called Regression-based Convolutional Neural Network-TPOT (R-CNN-TPOT) has been developed to perform the task of brain age prediction. Here, R-CNN-TPOT is obtained by combining the mathematical model of the Convolutional Neural Network (CNN) model and TPOT classification using regression modelling. In addition, the devised R-CNN-TPOT model provides better output with a Mean Absolute Error (MAE) of 0.033, Mean Square Error (MSE) of 0.063, R-squared of 15.456, and Root MSE (RMSE) of 0.251.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"159"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural correlates of social influence in persuasion process: a hyperscanning EEG study on negotiation.","authors":"Flavia Ciminaghi, Katia Rovelli, Carlotta Acconito, Michela Balconi","doi":"10.1007/s11571-025-10353-8","DOIUrl":"https://doi.org/10.1007/s11571-025-10353-8","url":null,"abstract":"<p><p>Group decision-making requires integrating different perspectives through persuasion, which involves unidirectional social influence, and negotiation, which is a reciprocal interaction based on cooperation and competition. While neuroscientific research has focused on identifying brain activations associated with these processes and their influencing factors, the impact of a prior persuasive dynamic on a subsequent negotiation task remains unexplored. This study examines whether engaging in a persuasive task, in which one individual has a role of social influence, affects neural activity during a subsequent negotiation. Using a hyperscanning paradigm with electroencephalography (EEG), frequency bands (delta, theta, alpha, beta and gamma) were analyzed in frontal, temporo-central and parieto-occipital regions in a sample of 26 participants. Results highlight distinct brain activation patterns between former persuaders and former receivers, with increased left-hemisphere delta activity and frontal theta and alpha activation in persuaders, while former receivers exhibited higher beta activity in the right parieto-occipital regions in the final stage of negotiation and higher gamma activity in right-lateralized regions. Overall, the study suggests that prior persuasive interactions shape subsequent negotiation at a neural level, influencing emotional, cognitive, and strategic engagement, with potential implications for understanding social dynamics in group interactions.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"165"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Deep neural networks and stochastic methods for cognitive modeling of rat behavioral dynamics in <ns0:math><ns0:mi>T</ns0:mi></ns0:math> -mazes.","authors":"Ali Turab, Josué-Antonio Nescolarde-Selva, Farhan Ullah, Andrés Montoyo, Cicik Alfiniyah, Wutiphol Sintunavarat, Doaa Rizk, Shujaat Ali Zaidi","doi":"10.1007/s11571-025-10247-9","DOIUrl":"https://doi.org/10.1007/s11571-025-10247-9","url":null,"abstract":"<p><p>Modeling animal decision-making requires mathematical rigor and computational analysis to capture underlying cognitive mechanisms. This study presents a cognitive model for rat decision-making behavior in <math><mi>T</mi></math> -mazes by combining stochastic methods with deep neural architectures. The model adapts Wyckoff's stochastic framework, originally grounded in Bush's discrimination learning theory, to describe probabilistic transitions between directional choices under reinforcement contingencies. The existence and uniqueness of solutions are demonstrated via fixed-point theorems, ensuring the formulation is well-posed. The asymptotic properties of the system are examined under boundary conditions to understand the convergence behavior of decision probabilities across trials. Empirical validation is performed using Monte Carlo simulations to compare expected trajectories with the model's predictive output. The dataset comprises spatial trajectory recordings of rats navigating toward food rewards under controlled experimental protocols. Trajectories are preprocessed through statistical filtering, augmented to address data imbalance, and embedded using t-SNE to visualize separability across behavioral states. A hybrid convolutional-recurrent neural network (CNN-LSTM) is trained on these representations and achieves a classification accuracy of 82.24%, outperforming conventional machine learning models, including support vector machines and random forests. In addition to discrete choice prediction, the network reconstructs continuous paths, enabling full behavioral sequence modeling from partial observations. The integration of stochastic dynamics and deep learning develops a computational basis for analyzing spatial decision-making in animal behavior. The proposed approach contributes to computational models of cognition by linking observable behavior to internal processes in navigational tasks.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"66"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12031716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143971746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A memristive synaptic circuit and optimization algorithm for synaptic control.","authors":"Seda Günakın, Zehra Gülru Çam Taşkıran","doi":"10.1007/s11571-025-10265-7","DOIUrl":"https://doi.org/10.1007/s11571-025-10265-7","url":null,"abstract":"<p><p>In order for the backpropagation training method, which is widely used for machine learning inference layer, to be directly applied to memristor crossbar arrays, either the weight change must be linear, or since the memristance change is not constant over time, the current memristance value must be kept in memory or changes must be controlled with an algorithm suitable for the used memristance function. To overcome the memory and energy drawbacks of this non-linearity, in this study, the parameters of a memristive circuit that can implement positive and negative weights were determined by the optimization method, using two charge-controlled mathematial memristor equations and a flux-controlled memristor emulator previously defined in the literature. In this way, the simplest linear control of weight change is achieved. Using the artificial bee colony algorithm, the passive element values of a circuit that can perform weight control up to 0.02 sensitivity and the duration of the applied control signal were determined. According to the experimental study, it was seen that weight control was achieved with a mean square error of 2.33 <math><mo>×</mo></math> 10^-4. Also the tracking rate of software-based test accuracy is 98.186%. With the proposed optimization method and cost function, linear control can be achieved by determining the parameters needed for online training with any memristor element.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"73"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12078898/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144076449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}