Cognitive Neurodynamics最新文献

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.

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.

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.

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.

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.

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.

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.

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.

The output of the basal ganglia to the corticothalamic system plays an important role in regulating absence seizures. Inspired by experiments, we systematically study the crucial roles of two newly identified direct inhibitory striatal-cortical projections that project from the striatal D1 nucleus (SD1) and striatal D2 nucleus (SD2) to the cerebral cortex, in controlling absence seizures. Through computational simulation, we observe that typical 2–4 Hz spike and wave discharges (SWDs) can be induced through the pathological mechanism of cortical circuits, and both enhancing the inhibitory coupling weight on the striatal-cortical projections and improving the discharge activation level of striatal populations can effectively control typical SWDs. Furthermore, typical SWDs can be suppressed by appropriately adjusting several input projections directly related to the striatum, through regulating the activation level of striatal populations. Interestingly, several indirect striatum-related basal ganglia projections also have significant effects on the inhibition of typical SWDs, through the direct inhibitory striatal-cortical projections. Both the unidirectional control mode and bidirectional control mode for typical SWDs exist in our modified model. Importantly, the enhancement of coupling strengths on inhibitory striatal-cortical projections is beneficial for suppressing SWDs and may play a decisive regulatory role in the formation of control modes. Therefore, our study suggests that striatum may be potential effective targets for the treatment of absence seizures, through two newly identified direct inhibitory striatal-cortical projections. Interestingly, we find that external stimuli simultaneously targeting the striatum and another basal ganglia nucleus have a better control effect on SWDs than targeting a single basal ganglia nucleus, and the obtained results provide testable hypotheses for future experiments.

The utilization of Electroencephalography (EEG) for emotion recognition has emerged as the primary tool in the field of affective computing. Traditional supervised learning methods are typically constrained by the availability of labeled data, which can result in weak generalizability of learned features. Additionally, EEG signals are highly correlated with human emotional states across temporal, spatial, and spectral dimensions. In this paper, we propose a Spatial-spEctral-Temporal based parallel Masked Autoencoder (SET-pMAE) model for EEG emotion recognition. SET-pMAE learns generic representations of spatial-temporal features and spatial-spectral features through a dual-branch self-supervised task. The reconstruction task of the spatial-temporal branch aims to capture the spatial-temporal contextual dependencies of EEG signals, while the reconstruction task of the spatial-spectral branch focuses on capturing the intrinsic spatial associations of the spectral domain across different brain regions. By learning from both tasks simultaneously, SET-pMAE can capture the generalized representations of features from the both tasks, thereby reducing the risk of overfitting. In order to verify the effectiveness of the proposed model, a series of experiments are conducted on the DEAP and DREAMER datasets. Results from experiments reveal that by employing self-supervised learning, the proposed model effectively captures more discriminative and generalized features, thereby attaining excellent performance.