Cogn Neurodyn [JOURNAL]
Sun
200 papers
Memristor-based RDBO-CNN circuit design and application of image multi-classification recognition.
Han G, Cheng G, Wang Y … +1 more , Sun J
Cogn Neurodyn · 2025 Dec · PMID 40837699 · Full text
Traditional convolutional neural networks used for classification largely rely on hyperparameter tuning and do not have the conditions for hardware implementation. Therefore, a memristor crossbar architecture circuit is...
Traditional convolutional neural networks used for classification largely rely on hyperparameter tuning and do not have the conditions for hardware implementation. Therefore, a memristor crossbar architecture circuit is proposed to implement the reinforced dung beetle optimization (RDBO) algorithm and the convolutional neural network (CNN). The circuit is composed of feeding module, storage module, ball rolling module, dance module, subpopulation module and CNN module. Traditional DBO algorithm with its adaptability and parallelism for CNN parameter optimization, there are some shortcomings. To solve the problem of unbalanced exploration and exploitation, the tendency to fall into local optimal state, an enhanced dung beetle optimization algorithm based on giant dung beetle and spiral search is proposed. The RDBO circuit is composed of feeding module, storage module, ball rolling module, dance module and subpopulation module. The CNN module is composed of convolution layer, pooling layer and fully connected layer, which is used to recognize and classify the image. The feasibility and accuracy of RDBO-CNN circuit are verified on MNIST image set. In order to further verify the effectiveness of the proposed circuit, simulation and comparison experiments are carried out the satellite image recognition RSI-CB image set which also has good accuracy. This will further promote the development and application of neural network technology.
Dynamic estimation of probability density using quantum neural network based on simple harmonic oscillator perturbed by an electric field.
Sagar G, Parthasarathy H, Agarwal V
Cogn Neurodyn · 2025 Dec · PMID 40814693 · Full text
In this research work quantum neural network using simple harmonic oscillator perturbed by an electric field is proposed. This work demonstrated that it is possible to generate a time varying wave function in Schrodinger...
In this research work quantum neural network using simple harmonic oscillator perturbed by an electric field is proposed. This work demonstrated that it is possible to generate a time varying wave function in Schrodinger's equation by controlling the electric field applied to a quantum harmonic oscillator, whose modulus square tracks a given probability density function (PDF). The adaptation scheme for the control electric field is generated via stochastic gradient algorithm. Statistical performance analysis of the algorithm is carried out using perturbation theory, i.e. by evaluating the shift in the control electric field under small perturbations of the PDF to be tracked. In addition "Fine tuning of converged electric field using large deviation principle (LDP)", " State variable form of the truncated Schrodinger equation" and " Dynamics of the electric field weight in terms of the state variable co-efficient vector" are also analysed. This work has application in data compression, PDF synthesis (for example synthesis of Electroencephalogram (EEG) PDF from speech PDF and visa versa).
DWT-OEFS: discrete wavelet transform based optimized ensemble feature selection for Parkinson's disease severity classification.
Agrawal S, Sahu SP
Cogn Neurodyn · 2025 Dec · PMID 40786005 · Full text
Parkinson's disease (PD) is a cognitive degenerative condition of central nervous system which highly impacts the motor function, resulting in gait dysfunction. Determining the severity of PD is essential for timely and...
Parkinson's disease (PD) is a cognitive degenerative condition of central nervous system which highly impacts the motor function, resulting in gait dysfunction. Determining the severity of PD is essential for timely and efficient medical management. Doctors often utilize clinical manifestations to grade the severity of PD using Hoehn & Yahr scale where their evaluation is heavily reliant on skill and experience. We propose an optimized ensemble metaheuristic-based feature selection framework by utilizing the signal processing techniques to grade the severity of PD on publicly available Physionet gait Vertical Ground Reaction Force dataset obtained using wearable device. Due to scarcity of medical dataset, the sample size is increased by segmentation of signal. Discrete wavelet transform (DWT) decomposes the signal and a total of 13 features including statistical, frequency and entropy-base are extracted. For an optimum subset of features, three bio-inspired metaheuristic algorithms Binary Grey Wolf Optimization, Binary Whale Optimization and Binary Dragonfly algorithm are used for optimized ensemble feature selection (OEFS) to prevent dimensionality curse thereby improving the classification accuracy. Further, the class imbalance issue is addressed via SMOTETomek and the selected features are then subjected to four best performing classifiers and weighted voting-based classifier. The suggested model is assessed using variety of performance assessment techniques like accuracy, precision, recall, F1-score and Mathew's Correlation Coefficient. The ensemble model achieves the maximum classification accuracy of 98.56% for multiclass classification through weighted voting. Our proposed approach outperforms existing models and individual classifiers, demonstrating its ability to accurately forecast and classify PD severity.
Cortical contribution related to top-down regulation in tone perception.
Zhang X, Tang Y, Wang H … +5 more , Huang Z, Tai W, Wong S, Chen Z, Long J
Cogn Neurodyn · 2025 Dec · PMID 40786004 · Full text
UNLABELLED: The top-down regulation of prior content facilitates the efficiency of following speech perception through the theta-band synchronization between higher-level cognitive regions and lower-level phonetic proces...
UNLABELLED: The top-down regulation of prior content facilitates the efficiency of following speech perception through the theta-band synchronization between higher-level cognitive regions and lower-level phonetic processing areas. However, how this regulation affects tone processing and its corresponding functional pathway remains unknown. In this study, we conducted three different auditory oddball paradigms which differed in prior constraints among Mandarin Chinese speakers. We calculated the amplitude of P3 differences caused by tone variations to evaluate the efficiency of tone processing within each paradigm. Theta-band functional connectivity (FC) related to lower-level phonetic processing areas was also analyzed at the source level to identify the specific top-down regulation loop. Our results showed that top-down regulation effects modulated responses to upcoming tonal processing reflected by smaller P3 amplitude differences with the occurrence of semantic priming. Results of FC analysis revealed different corresponding cortical contributions depending on priming content. Semantic-driven top-down regulation enhances FC between the the left caudal middle frontal gyrus and lower-level phonetic processing area. Moreover, when the prior constraint is semantically violated, enhanced FC between the left pars triangularis and the left supramarginal gyrus with lower-level phonetic processing regions were seen. Our study provides neurophysiological insights into the effects of top-down regulation on tone perception. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10314-1.
Leveraging low-frequency components for enhanced high-frequency steady-state visual evoked potential based brain computer interface in fast calibration scenario.
Chen Y, Xu R, Lau AT … +5 more , He X, Chen W, Wang X, Cichocki A, Jin J
Cogn Neurodyn · 2025 Dec · PMID 40761312 · Full text
High-frequency steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems offer improved user comfort but suffer from reduced performance compared to their low-frequency counterparts, limitin...
High-frequency steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems offer improved user comfort but suffer from reduced performance compared to their low-frequency counterparts, limiting their practical application. To address this issue, we propose a transfer learning-based method that leverages low-frequency SSVEP data to enhance high-frequency SSVEP performance. A filtering mechanism is designed to extract informative components from low-frequency signals, and the least squares algorithm is employed to generate high-quality synthetic high-frequency data. Experiments conducted on two public datasets using TDCA, eTRCA, and advanced TRCA-based algorithms demonstrate significant performance improvements. Our approach requires only two calibration trials, achieving 9.03% and 14.49% accuracy increases for eTRCA and TDCA in Dataset 1, and 13.91% and 14.53% improvements in Dataset 2, all within 1.5 s. Moreover, our approach effectively addresses the issue of single calibration data for high-frequency SSVEP-BCI systems. These results support the feasibility of fast calibration and improved performance in real-world high-frequency BCI applications.
A multi-model deep learning approach for human emotion recognition.
Arumugam L, Arumugam S, Chidambaram P … +1 more , Govindasamy K
Cogn Neurodyn · 2025 Dec · PMID 40761311 · Full text
Emotion recognition is a difficult problem mainly because emotions are presented in different modalities including; speech, face, and text. In light of this, in this paper, we introduce a novel framework known as Audio,...
Emotion recognition is a difficult problem mainly because emotions are presented in different modalities including; speech, face, and text. In light of this, in this paper, we introduce a novel framework known as Audio, Visual, and Text Emotions Fusion Network that will enhance the approaches to analyzing emotions that can incorporate these dissimilar types of inputs efficiently for the enhancement of the existing approaches to analyzing emotions. Using specialized techniques, each modality in this framework shows Graph Attention Network-based Transformer Network by employing Graph Attention Networks to detect dependencies in facial regions; Hybrid Wav2Vec 2.0 and Convolutional Neural Network combines Wav2Vec 2.0, and Convolutional Neural Network to extract informative temporal and frequency domain audio features. Contextual and sequential text semantics are captured by Bidirectional Encoder Representations from Transformers with Bidirectional Gated Recurrent Unit. They are fused based on a novel attention-based mechanism that distributes weights depending on the emotional context and improves cross-modal interactions. Moreover, the Audio, Visual, and Text Emotions Fusion Network system effectively identifies emotions, and the result section that contains overall accuracy at 98.7%, precision at 98.2%, recall, at 97.2%, and F1-score of 97.49% makes the proposed approach strong and efficient for real-time emotion recognition strategies.
An adaptive mechanism of improved heuristic algorithm and multiscale feature integration with residual GRU for emotion with mental health recognition.
Dedgaonkar SG, Kulkarni PV, Bhimanpallewar RN … +3 more , Shelke P, Bagade JV, Wawage PS
Cogn Neurodyn · 2025 Dec · PMID 40756004 · Full text
Globally, mental illnesses affect the individual peace of mind in multiple demographics. So, more precise identification of mental disease are termed as important for suggesting better treatment for the individual in the...
Globally, mental illnesses affect the individual peace of mind in multiple demographics. So, more precise identification of mental disease are termed as important for suggesting better treatment for the individual in the initial stage. Late diagnosis may result in harmful behavioral changes, suicidal thoughts, and death. To end this, an automated system of emotion with mental health recognition is introduced by an adaptive deep learning model. Firstly, the input texts are gathered from the online public data sources. Further, the collected data are undergoing the text pre-processing stage, where the unwanted, irrelevant data are removed. Subsequently, the pre-processed texts are fed as input to the feature extraction procedure. In this phase, the features are captured by the Bidirectional Long Short-Term Memory with Hierarchical Attention (BiLSTM-HA), Term Frequency-Inverse Document Frequency (TF-IDF), and Glove embedding. At the final stage of recognizing the emotions, these three features are subjected to the novel method, named Multiscale Fused Feature-based Adaptive Residual Gated Recurrent Unit (MFF-ARGRU). To attain the optimum performance, the hyper-parameters are optimally selected using the Improved Random Variable-based Sculptor Optimization Algorithm (IRV-SOA). Therefore, the performance of the system is validated using different measures and compared with baseline methodologies. Hence, in contrast, the proposed recognition model reveals that it achieves the high desired value of significantly analyzing the mental state of humans.
Interplay of flux-controlled memristive synapse and Josephson junction properties in modified Morris-Lecar neuron dynamics.
Ramasamy M, Selvi SS, Karthikeyan A … +2 more , Vijay SD, Rajagopal K
Cogn Neurodyn · 2025 Dec · PMID 40756003 · Full text
In this work, we investigate the role of magnetic flux and Josephson junction (JJ) properties in the network of modified Morris Lecar (mML) neuron model. We begin our analysis by plotting bifurcation and Lyapunov spectru...
In this work, we investigate the role of magnetic flux and Josephson junction (JJ) properties in the network of modified Morris Lecar (mML) neuron model. We begin our analysis by plotting bifurcation and Lyapunov spectrum for single coupled mML model. It exhibits both periodic and hyperchaotic dynamics for specific parameter ranges when considering both flux and the Josephson junction. Further, we plot the error plots to analyses the synchrony effect among neurons. Then we extend our analysis to a network of coupled mML neurons. Our study deals with three distinctive scenarios: Firstly, the regular network model where rewiring probability is zero, the nodes are symmetrically connect to their 5 closest neighbors on either sides. Secondly, the small world network model, in which we introduced a modification by assigning , causing 50 percentages of connected nodes to be randomly reconnected. Lastly, the random network model, where we pushed the rewiring probability to its maximum, resulting in each node being arbitrarily linked to 10 nodes. Collective behaviour of all the three cases will be discussed and analyzed using spatiotemporal plots and recurrence plots. Our analysis shows that when the nodes are connected randomly, a lower value of coupling strength in both flux and JJ is sufficient to achieve synchronous behavior among the neurons or nodes. However, when the nodes are connected in a regular manner, higher coupling strengths are required to achieve coherent behavior among the neurons.
Differential Impact of Repetitive Transcranial Magnetic Stimulation on Alzheimer's Disease Symptomology: Evidence from Electrovestibulography Does repetitive transcranial magnetic stimulation treatment alter Alzheimer's disease symptomology? a clue to show who will benefit.
Dastgheib ZA, Lithgow BJ, Moussavi ZK
Cogn Neurodyn · 2025 Dec · PMID 40756002 · Full text
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) has shown promise in enhancing cognitive function through neuroplasticity. This study investigates the impact of rTMS on Alzheimer's disease (AD) and AD wit...
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) has shown promise in enhancing cognitive function through neuroplasticity. This study investigates the impact of rTMS on Alzheimer's disease (AD) and AD with cerebrovascular disease (AD-CVD) symptomologies, using Electrovestibulography (EVestG). METHODOLOGY: Participants were recruited from a randomized, double-blind, placebo-controlled clinical trial on rTMS efficacy for mild to moderate AD. Thirty-five individuals who volunteered for the EVestG study (28 received active rTMS and 7 the sham treatment) were recorded at baseline, post-treatment, and two months' follow-up. EVestG recordings were analyzed to calculate normalized probability (NP) values for AD and AD-CVD symptomologies and compare with standard cognitive outcome. RESULTS: Changes in NP values from pre to post active treatment showed improved participants exhibited opposite trends in AD and AD-CVD symptomologies compared to non-improved participants with a decrease in NP and a slight increase in NP value. Significant associations were found between changes in cognitive score and NP values, even after adjusting for age, sex, and multiple comparisons, indicating that patients with higher certainty of AD diagnosis (versus AD-CVD) were more likely to benefit from rTMS. CONCLUSION: These findings suggest rTMS cognitive improvement may result from reduced AD-CVD symptomatology, especially in patients with higher certainty of AD diagnoses, potentially due to increased cerebral blood flow (CBF). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10310-5.
A CSDG photoelectronic transistor based on simulation model mimicking dopamine-facilitated synaptic plasticity for high energy-efficient neuromorphic system.
Ding QA, Gao Y, Liu C … +6 more , Gu C, Li X, Ning F, Hou B, Peng Y, Chen B
Cogn Neurodyn · 2025 Dec · PMID 40756001 · Full text
Charge-trap transistors are widely used for the simulation of biological synaptic functions. However, the unique structure of silicon-oxide-nitride-oxide-silicon (SONOS) makes it difficult to simulate short-term memory (...
Charge-trap transistors are widely used for the simulation of biological synaptic functions. However, the unique structure of silicon-oxide-nitride-oxide-silicon (SONOS) makes it difficult to simulate short-term memory (STM). Based on simulation modeling, this work proposes a cylindrical surrounding double-gate (CSDG) nanowire synaptic transistor with a Si N charge trap layer in direct contact with the channel. The synaptic functions of the enhanced weights are mimicked by modulating electrical impulses to achieve the short-term potentiation (STP) to long-term potentiation (LTP) transition. In addition, the post-synaptic response changes with light intensity and wavelength under light illumination, which is phenomenologically similar to light-assisted dopamine-promoted synaptic activity. Furthermore, the high blue light responsiveness successfully exhibits the physiological characteristic that blue light promotes more dopamine secretion in the retina of the human eye. This model introduces additional light stimulation to achieve dopamine dynamics driven learning acceleration, providing a foundation for improving the rapid recognition and learning ability of neural computing systems in the next step.
DSTA-Net: dynamic spatio-temporal feature augmentation network for motor imagery classification.
Chang L, Yang B, Zhang J … +3 more , Li T, Feng J, Xu W
Cogn Neurodyn · 2025 Dec · PMID 40718596 · Full text
Accurate decoding and strong feature interpretability of Motor Imagery (MI) are expected to drive MI applications in stroke rehabilitation. However, the inherent nonstationarity and high intra-class variability of MI-EEG...
Accurate decoding and strong feature interpretability of Motor Imagery (MI) are expected to drive MI applications in stroke rehabilitation. However, the inherent nonstationarity and high intra-class variability of MI-EEG pose significant challenges in extracting reliable spatio-temporal features. We proposed the Dynamic Spatio-Temporal Feature Augmentation Network (DSTA-Net), which combines DSTA and the Spatio-Temporal Convolution (STC) modules. In DSTA module, multi-scale temporal convolutional kernels tailored to the α and β frequency bands of MI neurophysiological characteristics, while raw EEG serve as a baseline feature layer to retain original information. Next, Grouped Spatial Convolutions extract multi-level spatial features, combined with weight constraints to prevent overfitting. Spatial convolution kernels map EEG channel information into a new spatial domain, enabling further feature extraction through dimensional transformation. And STC module further extracts features and conducts classification. We evaluated DSTA-Net on three public datasets and applied it to a self-collected stroke dataset. In tenfold cross-validation, DSTA-Net achieved average accuracy improvements of 6.29% ( < 0.01), 3.05% ( < 0.01), 5.26% ( < 0.01), and 2.25% over the ShallowConvNet on the BCI-IV-2a, OpenBMI, CASIA, and stroke dataset, respectively. In hold-out validation, DSTA-Net achieved average accuracy improvements of 3.99% ( < 0.01) and 4.2% ( < 0.01) over the ShallowConvNet on the OpenBMI and CASIA datasets, respectively. Finally, we applied DeepLIFT, Common Spatial Pattern, and t-SNE to analyze the contributions of individual EEG channels, extract spatial patterns, and visualize features. The superiority of DSTA-Net offers new insights for further research and application in MI. The code is available in https://github.com/CL-Cloud-BCI/DSTANet-code.
Novel EEG-based diagnostic framework for Major Depressive Disorder using microstate and entropy features.
Rahmati M, Jalaeianbanayan A, Vahedi J … +2 more , Najafzadeh H, Danesh B
Cogn Neurodyn · 2025 Dec · PMID 40718595 · Full text
This study aimed to develop a novel, non-invasive diagnostic framework for Major Depressive Disorder (MDD) by integrating EEG-based entropy and microstate features, transformed into image representations for deep learnin...
This study aimed to develop a novel, non-invasive diagnostic framework for Major Depressive Disorder (MDD) by integrating EEG-based entropy and microstate features, transformed into image representations for deep learning analysis. EEG data were obtained from 63 MDD patients and 36 healthy controls, collected from two publicly available datasets. To address class imbalance, data augmentation techniques including Gaussian noise addition, time warping, and frequency perturbation were applied to the healthy control group, resulting in a balanced dataset of 126 subjects. EEG signals were decomposed into five canonical frequency bands: delta (0.5-4 Hz), theta (5-8 Hz), alpha (9-13 Hz), beta (14-30 Hz), and gamma (31-50 Hz). Microstate segmentation was performed for each frequency band, yielding five microstates (A to E) that reflect distinct spatiotemporal brain dynamics. From these microstates, three key features (duration, occurrence, and coverage) were extracted alongside entropy measures (Shannon, sample, and approximate). All features were transformed into 2D image representations to capture spatial and temporal patterns and were subsequently classified using Convolutional Neural Networks (CNN), Deep Neural Networks, and Random Forest. Model performance was evaluated using fivefold cross-validation with accuracy and ROC-AUC metrics. The proposed framework enabled CNN models to achieve outstanding classification performance. Entropy-based images yielded an accuracy of 99.60 ± 0.22% and a ROC-AUC of 99.96 ± 0.02%, while microstate-based images achieved an accuracy of 96.96 ± 4.79% and ROC-AUC of 98.94 ± 2.27%. Notable group differences were observed in microstate dynamics, with microstate E exhibiting consistent reductions in both duration and occurrence among MDD patients, particularly within the alpha band at an 80-s window size (21.12% reduction in occurrence, < 0.01). Transition probability analysis revealed altered state-switching patterns in the beta and gamma bands. Discriminative activity was most prominent in the occipital and frontal regions across delta and gamma frequencies. Integrating entropy and microstate-derived features with deep learning presents a high-accuracy, non-invasive solution for diagnosing MDD. The study highlights significant disruptions in functional brain dynamics, particularly in the Default Mode Network and prefrontal areas, reflected through microstate alterations and entropy variability. These results provide deeper insight into the neurophysiological signatures of depression and support the development of objective EEG-based biomarkers to complement clinical psychiatric evaluations and guide personalized interventions.
Adaptive cholinergic feedback network oscillations: insights into striatal beta oscillations and circuit dynamics.
Wang Z, Qian D, Li S … +2 more , Lu W, Zhou D
Cogn Neurodyn · 2025 Dec · PMID 40718594 · Full text
Enhanced beta oscillations (12-25 Hz) within the cortico-basal ganglia-thalamic network are significantly associated with motor deficits and are a prominent characteristic of the neural dynamic pathology in Parkinson's d...
Enhanced beta oscillations (12-25 Hz) within the cortico-basal ganglia-thalamic network are significantly associated with motor deficits and are a prominent characteristic of the neural dynamic pathology in Parkinson's disease. Although the striatum has been proposed as a promising origin for enhanced beta oscillations, the precise mechanism through which distinct striatal neurons collaborate to orchestrate beta oscillations remains elusive. This study constructs a biophysical neural network model of the striatum based on experimental constraints. The model faithfully reproduces various experimental observations, including dopamine-dependent beta oscillations and phase-locked firing patterns. Through both theoretical and numerical analysis, our analysis reveals that striatal beta oscillations emerge from interactions within the cellular architecture, particularly the somatostatin-expressing interneurons (SOM) driven choline acetyltransferase-expressing interneurons (ChAT)-indirect pathway striatal projection neurons (iSPN) loop. Our results underscore the critical role of ChATs in enhancing beta oscillations. ChATs, instead of passively providing excitatory drive, actively amplify beta oscillations by enhancing their excitation efficacy through a phase-locked mode. Additionally, the inhibitory interactions among iSPNs, with robust and slow inhibitory recovery dynamics within iSPNs, potentially result in beta oscillations. The slow inhibitory recovery is likely attributed to the slow dynamics of the KCNQ current. SOMs further modulate the beta oscillations by affecting their downstream ChAT-iSPN loop. These results provide novel insights into the mechanism underlying striatal beta oscillations, shedding light on the processes involved in beta oscillations generation during pathological states.
Non-decomposition method for event-triggered finite-time synchronization control of complex-valued memristive neural networks.
Lin H, Shi Y, Guo J … +1 more , He X
Cogn Neurodyn · 2025 Dec · PMID 40703562 · Full text
This paper investigates the finite-time synchronization of complex-valued memristive neural networks (CVMNNs) with time-varying delays using an event-triggered control approach. The analysis is conducted in a holistic ma...
This paper investigates the finite-time synchronization of complex-valued memristive neural networks (CVMNNs) with time-varying delays using an event-triggered control approach. The analysis is conducted in a holistic manner, utilizing the one-norm and sign functions of complex numbers, thereby eliminating the need for decomposition. To alleviate communication pressure, an event-triggered controller is introduced, accompanied by specific conditions and criteria to guarantee synchronization within a finite time frame. Additionally, a direct estimate of the synchronization time is provided, and a positive lower bound on the minimum event interval is derived to prevent Zeno behavior. Building on this event-triggered strategy, a self-triggered mechanism is designed to eliminate the necessity for continuous monitoring. The proposed method is straightforward and easily implementable, with its effectiveness demonstrated through illustrative examples and simulation results.
Reward masks the learning of cognitive control demand.
Bustos B, Jiang J, Kool W
Cogn Neurodyn · 2025 Dec · PMID 40686546 · Full text
UNLABELLED: 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 a...
UNLABELLED: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10307-0.
Neurofusionnet: a comprehensive framework for accurate epileptic seizure prediction from EEG data with hybrid meta-heuristic optimization algorithm.
P S T, L S, J T … +1 more , K R V
Cogn Neurodyn · 2025 Dec · PMID 40661693 · Full text
This work uses cutting edge Electroencephalogram (EEG) data processing techniques to present a complete paradigm for epileptic seizure prediction. The methodology is a multi-step procedure that includes pre-processing, f...
This work uses cutting edge Electroencephalogram (EEG) data processing techniques to present a complete paradigm for epileptic seizure prediction. The methodology is a multi-step procedure that includes pre-processing, feature extraction, feature selection, and a new detection model based on deep learning for enhanced durability and accuracy. Bandpass filtering is used to reduce noise during the pre-processing phase, which improves the signal-to-noise ratio. EEG data quality is further improved using Independent Component Analysis, which finds and removes artifacts. Splitting continuous EEG data into fixed-duration segments, known as epoching, facilitates the investigation of discrete temporal patterns. Standard amplitude values are guaranteed by Z-score normalization, and seizure-related patterns are more sensitively detected when channels are selected using Common Spatial Patterns. Step one of the feature extraction processes involves statistical features and time-domain features. For spectrum information it is essential to recognizing seizures, frequency-domain features such as Power spectrum Density are extracted using a technique Fourier Transform. A full representation is obtained by extracting Time-Frequency Domain Features with the Wavelet Transform. Predictive power is increased by the efficient selection of discriminative characteristics through the use of a hybrid optimization model called Hybrid Chimp Enhanced Fox Optimization algorithm that combines optimization methods inspired by FOX and Chimp. The suggested NeuroFusionNet-based detection model combines Improved ShuffleNet V2, SqueezeNet, EfficientNet V2, and Multi Head Attention (MHA) based GhostNet V2, which captures complex patterns linked to epileptic episodes.
Effects of sleep deprivation on functional connectivity of olfactory related brain regions.
Han Q, Zhang P, Wen K … +7 more , Yang J, Zhang Y, Cao Q, Zhang T, Liu F, Weng X, Xu F
Cogn Neurodyn · 2025 Dec · PMID 40657134 · Full text
UNLABELLED: This study investigated the effects of 36 h of acute sleep deprivation on the functional connectivity of olfactory-related brain regions in healthy young males and examined the relationship between these effe...
UNLABELLED: This study investigated the effects of 36 h of acute sleep deprivation on the functional connectivity of olfactory-related brain regions in healthy young males and examined the relationship between these effects, individual alertness, and emotional state. Sixty participants underwent assessments both before and after sleep deprivation, including a psychomotor vigilance task, a sleepiness scale, a mood scale, and resting-state functional magnetic resonance imaging (fMRI). The results demonstrated that sleep deprivation significantly increased reaction times while reducing reaction speed. Participants exhibited increased sleepiness, particularly during rest, along with significant declines in tension and self-esteem. Notably, functional connectivity within olfactory-related brain regions was significantly disrupted, with alterations extending across multiple areas involved in cognition, emotion, and motor coordination. Specifically, the amygdala showed decreased functional connectivity with the inferior and superior cerebellum, pericalcarine cortex, and lingual gyrus, while the hippocampus exhibited decreased functional connectivity with the lingual gyrus, angular gyrus, middle temporal gyrus, and inferior cerebellum. Further correlation analyses revealed a complex interplay between the functional connectivity of these regions and participants' levels of vigilance, sleepiness, and mood. These findings provide new insights into the broader neurophysiological consequences of sleep deprivation on olfactory-related brain function. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10299-x.
Linear modeling of brain activity during selective attention to continuous speech: the critical role of the N1 effect in event-related potentials to acoustic edges.
Mai A, Hillyard SA, Strauss DJ
Cogn Neurodyn · 2025 Dec · PMID 40612893 · Full text
Recent studies have suggested a cortical representation of speech through superposition of evoked responses to acoustic edges, an idea closely related to regression-based modeling approaches for studying cortical synchro...
Recent studies have suggested a cortical representation of speech through superposition of evoked responses to acoustic edges, an idea closely related to regression-based modeling approaches for studying cortical synchronization to speech via magneto- or electroencephalography (M/EEG). However, it is still unclear to what extent speech-evoked event-related potentials (ERPs) contribute to these techniques. The present study addressed this question by re-analyzing an EEG data set obtained during a selective auditory attention task in which participants focused on one of two competing speakers. Segmenting the EEG based on acoustic edges revealed ERPs with clear P1-N1-P2 complexes and enhanced N1 components elicited by attended streams (). Comparisons between ERPs and regression results revealed that temporal response functions were highly similar spatiotemporally to the corresponding ERPs and that stimulus reconstruction accuracies were driven by a consistent enhancement of ERPs including the N1 effect. These observations point to a direct link between ERPs to acoustic edges in speech and the linear modeling techniques. In particular, the improvement in signal-to-noise ratio produced by consistent attention-related enhancements of the N1 component was found to be critical for achieving tracking of selectively attended speech, presumably facilitating the higher-order processing of the selected stream.
Effects of transcranial magneto-acoustical stimulation on excitatory and inhibitory neuronal discharge patterns.
Li Y, Qiu H, Zhu H
Cogn Neurodyn · 2025 Dec · PMID 40605916 · Full text
Transcranial Magneto-Acoustical Stimulation (TMAS) represents an innovative, highly efficacious, and non-invasive modality for brain stimulation. Neurons, as integral components of neural networks, are crucial for the tr...
Transcranial Magneto-Acoustical Stimulation (TMAS) represents an innovative, highly efficacious, and non-invasive modality for brain stimulation. Neurons, as integral components of neural networks, are crucial for the transmission of information. Nevertheless, the impact of TMAS on the discharge patterns of both excitatory and inhibitory neurons is not yet fully understood. To address this gap, the Hodgkin-Huxley neuronal model is analyzed using the improved Euler method. The neuronal discharge patterns are comprehensively examined by systematically adjusting ion channel parameters and stimulation parameters. The results indicate that ultrasound frequency exerted minimal influence on the properties of neuronal action potentials. Conversely, as the static magnetic field strength and ultrasound power are augmented, the excitability of both types of neurons progressively enhances. However, the changes in the electrical properties of action potentials are less pronounced in inhibitory neurons compared to excitatory neurons. Furthermore, alterations in ion channel parameters significantly influence the firing characteristics of both types of neurons. The present study elucidates that TMAS has a significant effect on the firing patterns of excitatory and inhibitory neurons. Excitatory neurons showed stronger regular discharges in response to static magnetic fields and increased ultrasound power, whereas inhibitory neurons did not respond to low-intensity static magnetic fields. In addition, our systematic analysis revealed synergistic effects between ion channel parameters and TMAS stimulation parameters. These findings shed light on how neuron type specificity and ion channel dynamics work together to shape the efficacy of TMAS, thus advancing previous studies.
Passivity and dissipativity-based fuzzy control of quaternion-valued memristive neural networks on time scales.
Li R, Cao J, Tu Z
Cogn Neurodyn · 2025 Dec · PMID 40605915 · Full text
In this paper, the problem of passivity and dissipativity analysis are investigated for a class of fractional-order quaternion-valued fuzzy memristive neural networks. By constructing proper Lyapunov functional and emplo...
In this paper, the problem of passivity and dissipativity analysis are investigated for a class of fractional-order quaternion-valued fuzzy memristive neural networks. By constructing proper Lyapunov functional and employing inequality technique, several improved passivity criteria and dissipativity conclusions are established, which can be checked efficiently by use of some standard mathematical calculations. Different from previous results, involving the quaternions connections, our derivation avoid considering the "magnitude" of quaternion. Finally, two simulation examples based on the fuzzy model are given to demonstrate the effectiveness of the proposed techniques.
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