Cognitive Neurodynamics最新文献

{"title":"Regulation of XOR function of reduced human L2/3 pyramidal neurons","authors":"Yanheng Li, Ruiming Zhang, Xiaojuan Sun","doi":"10.1007/s11571-024-10175-0","DOIUrl":"https://doi.org/10.1007/s11571-024-10175-0","url":null,"abstract":"<p>The apical dendrites of human L2/3 pyramidal neurons are capable of performing XOR computation by modulating the amplitude of dendritic calcium action potentials (dCaAPs) mediated by calcium ions. What influences this particular function? There is still no answer to this question. In this study, we employed a rational and feasible reduction method to successfully derive simplified models of human L2/3 pyramidal neurons while preserving their detailed functional properties. Using a conductance-based model, we manipulated the membrane potential of the apical dendrite in the simplified model. Our findings indicate that an increase in sodium conductance (<span>({g}_{Na})</span>) and membrane capacitance (<span>({C}_{m})</span>) weakens the XOR function, while regulation of potassium conductance (<span>({g}_{K})</span>) demonstrates robustness in maintaining the XOR function. Further analysis reveals that when a single pathway is activated, an increase in <span>({g}_{Na})</span> and <span>({C}_{m})</span> leads to decrease in the amplitude of dCaAPs, whereas increasing <span>({g}_{K})</span> has a relatively minor impact on dCaAPs amplitude. In conclusion, although calcium ions play a crucial role in enabling apical dendrites of human L2/3 pyramidal neurons to perform XOR computation, other ion channels’ conductance and membrane capacitance can also influence this function.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"9 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast-slow dynamics in a memristive ion channel-based bionic circuit","authors":"Xincheng Ding, Chengtao Feng, Ning Wang, Ao Liu, Quan Xu","doi":"10.1007/s11571-024-10168-z","DOIUrl":"https://doi.org/10.1007/s11571-024-10168-z","url":null,"abstract":"<p>Electrophysiological properties of ion channels can influence the transport process of ions and the generation of firing patterns in an excitable biological neuron when applying an external stimulus and exceeding the excitable threshold. In this paper, a current stimulus is employed to emulate the external stimulus, and a second-order locally active memristor (LAM) is deployed to characterize the properties of ion channels. Then, a simple bionic circuit possessing the LAM, a capacitor, a DC voltage, and the current stimulus is constructed. Fast-slow dynamical effects of the current stimulus with low- and high-frequency are respectively explored. Numerical simulations disclose that the bionic circuit can generate bursting behaviors for the low-frequency current stimulus and spiking behaviors for the high-frequency current stimulus. Besides, fold and Hopf bifurcation sets are deduced and the bifurcation mechanisms for bursting behaviors are elaborated. Furthermore, the numerically simulated bursting and spiking behaviors are verified by PCB-based hardware experiments. These results reflect the feasibility of the bionic circuit in generating the firing patterns of spiking and bursting behaviors and the external current can be employed to regulate these firing patterns.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"43 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation on the regular and chaotic dynamics of a ring network of five inertial Hopfield neural network: theoretical, analog and microcontroller simulation","authors":"Jean Baptiste Koinfo, Sridevi Sriram, Kengne Jacques, Anitha Karthikeyan","doi":"10.1007/s11571-024-10170-5","DOIUrl":"https://doi.org/10.1007/s11571-024-10170-5","url":null,"abstract":"<p>The studies conducted in this contribution are based on the analysis of the dynamics of a homogeneous network of five inertial neurons of the Hopfield type to which a unidirectional ring coupling topology is applied. The coupling is achieved by perturbing the next neuron's amplitude with a signal proportional to the previous one. The system consists of ten coupled ODEs, and the investigations carried out have allowed us to highlight several unusual and rarely related dynamics, hence the importance of emphasizing them. The main analysis tools that have helped in obtaining the results presented are phase portraits, bifurcation diagrams, and the Maximal Lyapunov exponent. In this system, we have observed phenomena such as the coexistence of homogeneous and heterogeneous attractors, period-doubling crisis, parallel branches, and the path leading to hyperchaotic multi-spiral. All attractors are non-hidden as they originate from well-known equilibrium points. The system has 254 equilibrium points, among which only 32 undergo a Hopf bifurcation followed by period-doubling, leading to a merging crisis phenomenon until the final hyperchaotic multi-spiral attractor. For the same parameter values (coupling or dissipation), a maximum of 30 attractors for the coupling coefficient and 32 attractors for dissipation coexist, and illustrated by the phase portraits. Virtual verification using Pspice and practical verification using an Arduino Mega 2580 microcontroller of the model have also been reported. They are in perfect agreement with the behaviors resulting from numerical investigations. The circuit energy and dimensionless energy has been estimated and the scale relation established. The results presented further enrich previous and recent work in the study of the nonlinear dynamics of Hopfield-type neural networks. Additionally, it is important to mention that cyclic coupling typology may be used as an alternative approach in generating multi-spiral signals in Hopfield oscillators.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"67 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review","authors":"Nimra Rahman, Danish Mahmood Khan, Komal Masroor, Mehak Arshad, Amna Rafiq, Syeda Maham Fahim","doi":"10.1007/s11571-024-10167-0","DOIUrl":"https://doi.org/10.1007/s11571-024-10167-0","url":null,"abstract":"<p>Numerous individuals encounter challenges in verbal communication due to various factors, including physical disabilities, neurological disorders, and strokes. In response to this pressing need, technology has actively pursued solutions to bridge the communication gap, recognizing the inherent difficulties faced in verbal communication, particularly in contexts where traditional methods may be inadequate. Electroencephalogram (EEG) has emerged as a primary non-invasive method for measuring brain activity, offering valuable insights from a cognitive neurodevelopmental perspective. It forms the basis for Brain-Computer Interfaces (BCIs) that provide a communication channel for individuals with neurological impairments, thereby empowering them to express themselves effectively. EEG-based BCIs, especially those adapted to decode imagined speech from EEG signals, represent a significant advancement in enabling individuals with speech disabilities to communicate through text or synthesized speech. By utilizing cognitive neurodevelopmental insights, researchers have been able to develop innovative approaches for interpreting EEG signals and translating them into meaningful communication outputs. To aid researchers in effectively addressing this complex challenge, this review article synthesizes key findings from state-of-the-art significant studies. It investigates into the methodologies employed by various researchers, including preprocessing techniques, feature extraction methods, and classification algorithms utilizing Deep Learning and Machine Learning approaches and their integration. Furthermore, the review outlines the potential avenues for future research, with the goal of advancing the practical implementation of EEG-based BCI systems for decoding imagined speech from a cognitive neurodevelopmental perspective.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"8 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding of movement-related cortical potentials at different speeds","authors":"Jing Zhang, Cheng Shen, Weihai Chen, Xinzhi Ma, Zilin Liang, Yue Zhang","doi":"10.1007/s11571-024-10164-3","DOIUrl":"https://doi.org/10.1007/s11571-024-10164-3","url":null,"abstract":"<p>The decoding of electroencephalogram (EEG) signals, especially motion-related cortical potentials (MRCP), is vital for the early detection of motor intent before movement execution. To enhance the decoding accuracy of MRCP and promote the application of early motion intention in active rehabilitation training, we propose a method for decoding MRCP signals. Specifically, an experimental paradigm is designed for the efficient capture of MRCP signals. Moreover, a feature extraction method based on differentiation is proposed to effectively characterize action variability. Six subjects were recruited to validate the effectiveness of the decoding method. Experiments such as fixed-window classification, sliding-window detection, and asynchronous analysis demonstrate that the method can detect motion intention 316 milliseconds before action execution and is capable of continuously detecting both rapid and slow movements.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"5 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alternating chimera states and synchronization in multilayer neuronal networks with ephaptic intralayer coupling","authors":"Heng Li, Yong Xie","doi":"10.1007/s11571-024-10169-y","DOIUrl":"https://doi.org/10.1007/s11571-024-10169-y","url":null,"abstract":"<p>Over the past decade, most of researches on the communication between the neurons are based on synapses. However, the changes in action potentials in neurons may produce complex electromagnetic fields in the media, which may also have an impact on the electrical activity of neurons. To explore this factor, we construct a two-layer neuronal network composed of identical Hindmarsh–Rose neurons. Each neuron is connected with its neighbors in the layer via magnetic connections and a neuron in the corresponding position of the other layer via electrical synapse. By adjusting the electrical coupling strength and magnetic coupling strength, we find the appearance of alternating chimera states and transient chimera states whenever the intralayer coupling is nonlocal and local, respectively. According to our study, these phenomena have not been studied in multilayer networks of this structure. And it is found that the transient chimera states only could occur when the number of coupled neighbors is small. In addition, the states of two independent networks will affect the final states of networks applying the same sufficiently large interlayer coupling strength. Our study reveals a possible effect of electrical coupling and ephaptic coupling produced together on the dynamic behavior of the neuronal networks. Meanwhile, our results suggest that it makes sense to take electromagnetic induction into neuronal models.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"13 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synaptic effects on the intermittent synchronization of gamma rhythms","authors":"Quynh-Anh Nguyen, Leonid L. Rubchinsky","doi":"10.1007/s11571-024-10150-9","DOIUrl":"https://doi.org/10.1007/s11571-024-10150-9","url":null,"abstract":"<p>Synchronization of neural activity in the gamma frequency band is associated with various cognitive phenomena. Abnormalities of gamma synchronization may underlie symptoms of several neurological and psychiatric disorders such as schizophrenia and autism spectrum disorder. Properties of neural oscillations in the gamma band depend critically on the synaptic properties of the underlying circuits. This study explores how synaptic properties in pyramidal-interneuronal circuits affect not only the average synchronization strength but also the fine temporal patterning of neural synchrony. If two signals show only moderate synchrony strength, it may be possible to consider these dynamics as alternating between synchronized and desynchronized states. We use a model of connected circuits that produces pyramidal-interneuronal gamma oscillations to explore the temporal patterning of synchronized and desynchronized intervals. Changes in synaptic strength may alter the temporal patterning of synchronized dynamics (even if the average synchrony strength is not changed). Larger values of local synaptic connections promote longer desynchronization durations, while larger values of long-range synaptic connections promote shorter desynchronization durations. Furthermore, we show that circuits with different temporal patterning of synchronization may have different sensitivity to synaptic input. Thus, the alterations of synaptic strength may mediate physiological properties of neural circuits not only through change in the average synchrony level of gamma oscillations, but also through change in how synchrony is patterned in time over very short time scales.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"28 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the energy of dissipative neural systems","authors":"Erik D. Fagerholm, Robert Leech, Federico E. Turkheimer, Gregory Scott, Milan Brázdil","doi":"10.1007/s11571-024-10166-1","DOIUrl":"https://doi.org/10.1007/s11571-024-10166-1","url":null,"abstract":"<p>There is, at present, a lack of consensus regarding precisely what is meant by the term 'energy' across the sub-disciplines of neuroscience. Definitions range from deficits in the rate of glucose metabolism in consciousness research to regional changes in neuronal activity in cognitive neuroscience. In computational neuroscience virtually all models define the energy of neuronal regions as a quantity that is in a continual process of dissipation to its surroundings. This, however, is at odds with the definition of energy used across all sub-disciplines of physics: a quantity that does not change as a dynamical system evolves in time. Here, we bridge this gap between the dissipative models used in computational neuroscience and the energy-conserving models of physics using a mathematical technique first proposed in the context of fluid dynamics. We go on to derive an expression for the energy of the linear time-invariant (LTI) state space equation. We then use resting-state fMRI data obtained from the human connectome project to show that LTI energy is associated with glucose uptake metabolism. Our hope is that this work paves the way for an increased understanding of energy in the brain, from both a theoretical as well as an experimental perspective.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"71 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cross-attention swin transformer network for EEG-based subject-independent cognitive load assessment","authors":"Zhongrui Li, Rongkai Zhang, Li Tong, Ying Zeng, Yuanlong Gao, Kai Yang, Bin Yan","doi":"10.1007/s11571-024-10160-7","DOIUrl":"https://doi.org/10.1007/s11571-024-10160-7","url":null,"abstract":"<p>EEG signals play a crucial role in assessing cognitive load, which is a key element in ensuring the secure operation of human–computer interaction systems. However, the variability of EEG signals across different subjects poses a challenge in applying the pre-trained cognitive load assessment model to new subjects. Moreover, previous domain adaptation research has primarily focused on developing complex network architectures to learn more domain-invariant features, overlooking the noise introduced by pseudo-labels and the challenges posed by domain migration problems. Therefore, this study proposes a novel cross-attention swin-transformer network for cross-subject cognitive load assessment, which achieves inter-domain feature alignment through parameter sharing in cross attention mechanism without using pseudo-labels, and utilizes maximum mean discrepancy (MMD) to measure the difference between the feature distributions of the source and target domains, further promoting feature alignment between domains. This method aims to leverage the advantages of cross-attention mechanism and MMD to better mitigate individual differences among subjects in cross-subject cognitive workload assessment. To validate the classification performance of the proposed network, two datasets of image recognition task and N-back task were employed for testing. Results show that, the proposed model outperformed advanced methods with cross-subject classification results of 88.13% and 81.27% on the on local and public datasets. The ablation experiment results reveal that using either the cross-attention mechanism or the MMD strategy alone improves cross-subject classification performance by 2.11% and 2.95% on the local dataset, respectively. Furthermore, the results of the EEG features distribution differences between all subjects before and after network training showed a significant reduction in feature distribution differences between subjects, further confirming the network’s effectiveness in minimizing inter-subject differences.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"57 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bimodal deep learning network based on CNN for fine motor imagery","authors":"Chenyao Wu, Yu Wang, Shuang Qiu, Huiguang He","doi":"10.1007/s11571-024-10159-0","DOIUrl":"https://doi.org/10.1007/s11571-024-10159-0","url":null,"abstract":"<p>Motor imagery (MI) is an important brain-computer interface (BCI) paradigm. The traditional MI paradigm (imagining different limbs) limits the intuitive control of the outer devices, while fine MI paradigm (imagining different joint movements from the same limb) can control the mechanical arm without cognitive disconnection. However, the decoding performance of fine MI limits its application. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are widely used in BCI systems because of their portability and easy operation. In this study, a fine MI paradigm including four classes (hand, wrist, shoulder and rest) was designed, and the data of EEG-fNIRS bimodal brain activity was collected from 12 subjects. Event-related desynchronization (ERD) from EEG signals shows a contralateral dominant phenomenon, and there is difference between the ERD of the four classes. For fNIRS signal in the time dimension, the time periods with significant difference can be observed in the activation patterns of four MI tasks. Spatially, the signal peak based brain topographic map also shows difference of these four MI tasks. The EEG signal and fNIRS signal of these four classes are distinguishable. In this study, a bimodal fusion network is proposed to improve the fine MI tasks decoding performance. The features of these two modalities are extracted separately by two feature extractors based on convolutional neural networks (CNN). The recognition performance was significantly improved by the bimodal method proposed in this study, compared with the performance of the single-modal network. The proposed method outperformed all comparison methods, and achieved a four-class accuracy of 58.96%. This paper demonstrates the feasibility of EEG and fNIRS bimodal BCI systems for fine MI, and shows the effectiveness of the proposed bimodal fusion method. This research is supposed to support fine MI-based BCI systems with theories and techniques.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"11 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}