Cognitive Neurodynamics最新文献

{"title":"Lemniscate of Bernoulli's map quantifiers: innovative measures for EEG emotion recognition.","authors":"Atefeh Goshvarpour, Ateke Goshvarpour","doi":"10.1007/s11571-023-09968-6","DOIUrl":"10.1007/s11571-023-09968-6","url":null,"abstract":"<p><p>Thanks to the advent of affective computing, designing an automatic human emotion recognition system for clinical and non-clinical applications has attracted the attention of many researchers. Currently, multi-channel electroencephalogram (EEG)-based emotion recognition is a fundamental but challenging issue. This experiment envisioned developing a new scheme for automated EEG affect recognition. An innovative nonlinear feature engineering approach was presented based on Lemniscate of Bernoulli's Map (LBM), which belongs to the family of chaotic maps, in line with the EEG's nonlinear nature. As far as the authors know, LBM has not been utilized for biological signal analysis. Next, the map was characterized using several graphical indices. The feature vector was imposed on the feature selection algorithm while evaluating the role of the feature vector dimension on emotion recognition rates. Finally, the efficiency of the features on emotion recognition was appraised using two conventional classifiers and validated using the Database for Emotion Analysis using Physiological signals (DEAP) and SJTU Emotion EEG Dataset-IV (SEED-IV) benchmark databases. The experimental results showed a maximum accuracy of 92.16% for DEAP and 90.7% for SEED-IV. Achieving higher recognition rates compared to the state-of-art EEG emotion recognition systems suggest the proposed method based on LBM could have potential both in characterizing bio-signal dynamics and detecting affect-deficit disorders.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143135/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48572731","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":"Modification of temporal pattern sensitivity for inputs from medial entorhinal cortex by lateral inputs in hippocampal granule cells.","authors":"Naoki Nakajima, Tadanobu Kamijo, Hirofumi Hayakawa, Eriko Sugisaki, Takeshi Aihara","doi":"10.1007/s11571-023-09964-w","DOIUrl":"10.1007/s11571-023-09964-w","url":null,"abstract":"<p><p>The medial dendrites (MDs) of granule cells (GCs) receive spatial information through the medial entorhinal cortex (MEC) from the entorhinal cortex in the rat hippocampus while the distal dendrites (DDs) of GCs receive non-spatial information (sensory inputs) through the lateral entorhinal cortex (LEC). However, it is unclear how information processing through the two pathways is managed in GCs. In this study, we investigated associative information processing between two independent inputs to MDs and DDs. First, in physiological experiments, to compare response characteristics between MDs and DDs, electrical stimuli comprising five pulses were applied to the MPP or LPP in rat hippocampal slices. These stimuli transiently decreased the excitatory postsynaptic potentials (EPSPs) of successive input pulses to MDs, whereas EPSPs to DDs showed sustained responses. Next, in computational experiments using a local network model obtained by fitting of the physiological experimental data, we compared associative information processing between DDs and MDs. The results showed that the temporal pattern sensitivity for burst inputs to MDs depended on the frequency of the random pulse inputs applied to DDs. On the other hand, with lateral inhibition to GCs from interneurons, the temporal pattern sensitivity for burst inputs to MDs was enhanced or tuned up according to the frequency of the random pulse inputs to the other cells. Thus, our results suggest that the temporal pattern sensitivity of spatial information depends on the non-spatial inputs to GCs.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43294974","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":"Detections of bifurcation in a fractional-order Cohen-Grossberg neural network with multiple delays.","authors":"Chengdai Huang, Shansong Mo, Jinde Cao","doi":"10.1007/s11571-023-09934-2","DOIUrl":"10.1007/s11571-023-09934-2","url":null,"abstract":"<p><p>The dynamics of integer-order Cohen-Grossberg neural networks with time delays has lately drawn tremendous attention. It reveals that fractional calculus plays a crucial role on influencing the dynamical behaviors of neural networks (NNs). This paper deals with the problem of the stability and bifurcation of fractional-order Cohen-Grossberg neural networks (FOCGNNs) with two different leakage delay and communication delay. The bifurcation results with regard to leakage delay are firstly gained. Then, communication delay is viewed as a bifurcation parameter to detect the critical values of bifurcations for the addressed FOCGNN, and the communication delay induced-bifurcation conditions are procured. We further discover that fractional orders can enlarge (reduce) stability regions of the addressed FOCGNN. Furthermore, we discover that, for the same system parameters, the convergence time to the equilibrium point of FONN is shorter (longer) than that of integer-order NNs. In this paper, the present methodology to handle the characteristic equation with triple transcendental terms in delayed FOCGNNs is concise, neoteric and flexible in contrast with the prior mechanisms owing to skillfully keeping away from the intricate classified discussions. Eventually, the developed analytic results are nicely showcased by the simulation examples.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44143185","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":"Assessment of impaired consciousness using EEG-based connectivity features and convolutional neural networks.","authors":"Lihui Cai, Xile Wei, Yang Qing, Meili Lu, Guosheng Yi, Jiang Wang, Yueqing Dong","doi":"10.1007/s11571-023-09944-0","DOIUrl":"10.1007/s11571-023-09944-0","url":null,"abstract":"<p><p>Growing electroencephalogram (EEG) studies have linked the abnormities of functional brain networks with disorders of consciousness (DOC). However, due to network data's high-dimensional and non-Euclidean properties, it is difficult to exploit the brain connectivity information that can effectively detect the consciousness levels of DOC patients via deep learning. To take maximum advantage of network information in assessing impaired consciousness, we utilized the functional connectivity with convolutional neural network (CNN) and employed three rearrangement schemes to improve the evaluation performance of brain networks. In addition, the gradient-weighted class activation mapping (Grad-CAM) was adopted to visualize the classification contributions of connections among different areas. We demonstrated that the classification performance was significantly enhanced by applying network rearrangement techniques compared to those obtained by the original connectivity matrix (with an accuracy of 75.0%). The highest classification accuracy (87.2%) was achieved by rearranging the alpha network based on the anatomical regions. The inter-region connections (i.e., frontal-parietal and frontal-occipital connectivity) played dominant roles in the classification of patients with different consciousness states. The effectiveness of functional connectivity in revealing individual differences in brain activity was further validated by the correlation between behavioral performance and connections among specific regions. These findings suggest that our proposed assessment model could detect the residual consciousness of patients.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42072560","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":"Linear mixed-effect models for correlated response to process electroencephalogram recordings.","authors":"Vanesa B Meinardi, Juan M Díaz López, Hugo Diaz Fajreldines, Carina Boyallian, Monica Balzarini","doi":"10.1007/s11571-023-09984-6","DOIUrl":"10.1007/s11571-023-09984-6","url":null,"abstract":"<p><p>A data set of clinical studies of electroencephalogram recordings (EEG) following data acquisition protocols in control individuals (Eyes Closed Wakefulness - Eyes Open Wakefulness, Hyperventilation, and Optostimulation) are quantified with information theory metrics, namely permutation Shanon entropy and permutation Lempel Ziv complexity, to identify functional changes. This work implement Linear mixed-effects models (LMEMs) for confirmatory hypothesis testing. The results show that EEGs have high variability for both metrics and there is a positive correlation between them. The mean of permutation Lempel-Ziv complexity and permutation Shanon entropy used simultaneously for each of the four states are distinguishable from each other. However, used separately, the differences between permutation Lempel-Ziv complexity or permutation Shanon entropy of some states were not statistically significant. This shows that the joint use of both metrics provides more information than the separate use of each of them. Despite their wide use in medicine, LMEMs have not been commonly applied to simultaneously model metrics that quantify EEG signals. Modeling EEGs using a model that characterizes more than one response variable and their possible correlations represents a new way of analyzing EEG data in neuroscience.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44867948","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":"Correction to: Population and individual firing behaviors in sparsely synchronized rhythms in the hippocampal dentate gyrus.","authors":"Sang-Yoon Kim, Woochang Lim","doi":"10.1007/s11571-023-09949-9","DOIUrl":"10.1007/s11571-023-09949-9","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1007/s11571-021-09728-4.].</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"52867040","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":"Brain connectivity dynamics during listening to music and potential impact on task performance.","authors":"Geethanjali Balasubramanian, Adalarasu Kanagasabai, Mahesh Veezhinathan, Jagannath Mohan","doi":"10.1007/s11571-023-09948-w","DOIUrl":"10.1007/s11571-023-09948-w","url":null,"abstract":"<p><p>To analyze brain connectivity dynamics during listening to music and estimate the potential impact on task performance. Fifteen participants (13 males and 2 females) participated in this study based on their interest in Indian classical music. Measurements of the influence of Indian music on task performance were obtained by assessing brain activation using EEG signals. Brain connectivity analysis was performed to visualize the connections between brain regions under various experimental conditions. Visual Go/No Go Stimuli was used to evaluate visual spatial attention during operation by evaluating misses, committed errors, and reaction times. In Task 1 (listening to music only), it was reported that there was a change in the positions of the electrodes (F3, F7) located in the left frontal lobe. The energy of the relative beta component was significantly higher only at F7 during task 1 (<i>p</i> = 0.005). Event-related desynchronization alpha and theta synchronization were significant (<i>p</i> = 0.005) at all electrode sites in the bilateral frontal lobes (F3, F4, F7 and F8) while listening to music and performing tasks (task 2). When the task without music (task 3) was performed, the energy of the relative alpha component was significantly higher at the Fp2 electrode position (<i>p</i> = 0.005). It is noteworthy that the energy of the theta component was significantly lower at the location of the Fp2 electrode (<i>p</i> = 0.005). The frontal asymmetry index score measures were significantly high at F4/F3 and F8/F7 during task 1. The connectivity map of theta synchronization showed a robust association between Fp2 and F8 which was in turn connected to P4 and O2 during Task 2. Results indicated an increased omission and commission errors during Task 3.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143124/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45662805","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":"Recognition of autism spectrum disorder in children based on electroencephalogram network topology.","authors":"Fali Li, Shu Zhang, Lin Jiang, Keyi Duan, Rui Feng, Yingli Zhang, Gao Zhang, Yangsong Zhang, Peiyang Li, Dezhong Yao, Jiang Xie, Wenming Xu, Peng Xu","doi":"10.1007/s11571-023-09962-y","DOIUrl":"10.1007/s11571-023-09962-y","url":null,"abstract":"<p><p>Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46381779","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":"The neuronal and synaptic dynamics underlying post-inhibitory rebound burst related to major depressive disorder in the lateral habenula neuron model.","authors":"Kaihua Ma, Huaguang Gu, Yanbing Jia","doi":"10.1007/s11571-023-09960-0","DOIUrl":"10.1007/s11571-023-09960-0","url":null,"abstract":"<p><p>A burst behavior observed in the lateral habenula (LHb) neuron related to major depressive disorder has attracted much attention. The burst is induced from silence by the excitatory <i>N</i>-methyl-D-aspartate (NMDA) synapse or by the inhibitory stimulation, i.e., a post-inhibitory rebound (PIR) burst, which has not been explained clearly. In the present paper, the neuronal and synaptic dynamics for the PIR burst are acquired in a theoretical neuron model. At first, dynamic cooperations between the fast rise of inhibitory γ-aminobutyric acid (GABA) synapse, slow rise of NMDA synapse, and T-type calcium current to evoke the PIR burst are obtained. Similar to the inhibitory pulse stimulation, fast rising GABA current can reduce the membrane potential to a level low enough to de-inactivate the low threshold T-type calcium current to evoke a PIR spike, which can enhance the slow rising NMDA current activated at a time before or after the PIR spike. The NMDA current following the PIR spike exhibits slow decay to induce multiple spikes to form the PIR burst. Such results present a theoretical explanation and a candidate for the PIR burst in real LHb neurons. Then, the dynamical mechanism for the PIR spike mediated by the T-type calcium channel is obtained. At large conductance of T-type calcium channel, the resting state corresponds to a stable focus near Hopf bifurcation and exhibits an \"uncommon\" threshold curve with membrane potential much lower than the resting membrane potential. Inhibitory modulation induces membrane potential decreased to run across the threshold curve to evoke the PIR spike. At small conductance of the T-type calcium channel, a stable node appears and manifests a common threshold curve with higher membrane potential, resulting in non-PIR phenomenon. The results present the dynamic cooperations between neuronal dynamics and fast/slow dynamics of different synapses for the PIR burst observed in the LHb neuron, which is helpful for the modulations to major depressive disorder.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143169/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48614763","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":"Effect in the spectra of eigenvalues and dynamics of RNNs trained with excitatory-inhibitory constraint.","authors":"Cecilia Jarne, Mariano Caruso","doi":"10.1007/s11571-023-09956-w","DOIUrl":"10.1007/s11571-023-09956-w","url":null,"abstract":"<p><p>In order to comprehend and enhance models that describes various brain regions it is important to study the dynamics of trained recurrent neural networks. Including Dale's law in such models usually presents several challenges. However, this is an important aspect that allows computational models to better capture the characteristics of the brain. Here we present a framework to train networks using such constraint. Then we have used it to train them in simple decision making tasks. We characterized the eigenvalue distributions of the recurrent weight matrices of such networks. Interestingly, we discovered that the non-dominant eigenvalues of the recurrent weight matrix are distributed in a circle with a radius less than 1 for those whose initial condition before training was random normal and in a ring for those whose initial condition was random orthogonal. In both cases, the radius does not depend on the fraction of excitatory and inhibitory units nor the size of the network. Diminution of the radius, compared to networks trained without the constraint, has implications on the activity and dynamics that we discussed here.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-023-09956-w.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143133/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49009323","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}