Cognitive Neurodynamics最新文献

Currently, the research in memristor-based associative memory neural networks pays more attention to positive stimuli and lays less attention to negative stimuli. Negative stimuli are superior to positive stimuli in some ways, but lack the associated circuit implementation. In this paper, a memristor-based circuit design of avoidance learning with time delay is designed. The circuit can respond to a negative stimulus after initial avoidance learning and the effect of delay time between stimuli is considered. The realization of avoidance learning is confirmed in the PSPICE simulation results. In addition, an extended application circuit based on the memristor-based circuit design of avoidance learning with time delay is proposed. The application circuit is based on the advantage of negative stimuli is more difficult to forget than positive stimuli in associative memory. Based on the features of objects as input, the output of the circuit is used to achieve the function of avoidance learning. The application circuit provides more references for neural networks of automatic driving with further development.

Long-term training induces neural plasticity in the visual cognitive processing cortex of table tennis athletes, who perform cognitive processing in a resource-conserving manner. However, further discussion is needed to determine whether the spatial processing advantage of table tennis players manifests in the early stage of sensory input or the late stage of processing. This study aims to explore the processing styles and neural activity characteristics of table tennis players during spatial cognitive processing. Spatial cognitive tasks were completed by 28 college students and 20 s-level table tennis players, and event-related potentials (ERP) data were recorded during the task. The behavioral results showed that the table tennis group performed better on the task than the college students group (control). The ERP results showed that the amplitude of the N1 component of the table tennis group was significantly lower than that of the control group. The amplitude of the P2 and P3 components of the table tennis group was higher than that of the control group. Table tennis players showed significant synergistic activity between electrodes in the β-band. The results of this study suggest that table tennis players significantly deploy attentional resources and cognitive control. Further, they employ stored motor experience to process spatial information in a hierarchical predictive coding manner.

Two types of neuron models are constructed in this paper, namely the single discrete memristive synaptic neuron model and the dual discrete memristive synaptic neuron model. Firstly, it is proved that both models have only one unstable equilibrium point. Then, the influence of the coupling strength parameters and neural membrane amplification coefficient of the corresponding system of the two models on the rich dynamical behavior of the systems is analyzed. Research has shown that when the number of discrete local active memristor used as simulation synapses in the system increases from one to two, the coupling strength parameter of the same memristor has significantly different effects on the dynamical behavior of the system within the same range, that is, from a state with periodicity, chaos, and periodicity window to a state with only chaos. In addition, under the influence of coupling strength parameters and neural membrane amplification coefficients, the complexity of the system weakens to varying degrees. Moreover, under the effect of two memristors, the system exhibits a rare and interesting phenomenon where the coupling strength parameter and the neural membrane amplification coefficient can mutually serve as control parameter, resulting in the generation of a remerging Feigenbaum tree. Finally, the pseudo-randomness of the chaotic systems corresponding to the two models are detected by NIST SP800-22, and relevant simulation results are verified on the DSP hardware experimental platform. The discrete memristive synaptic neuron models established in this article provide assistance in studying the relevant working principles of real neurons.

Autism Spectrum Disorder(ASD) is a type of neurological disorder that is common among children. The diagnosis of this disorder at an early stage is the key to reducing its effects. The major symptoms include anxiety, lack of communication, and less social interaction. This paper presents a systematic review conducted based on PRISMA guidelines for automated diagnosis of ASD. With rapid development in the field of Data Science, numerous methods have been proposed that can diagnose the disease at an early stage which can minimize the effects of the disorder. Machine learning and deep learning have proven suitable techniques for the automated diagnosis of ASD. These models have been developed on various datasets such as ABIDE I and ABIDE II, a frequently used dataset based on rs-fMRI images. Approximately 26 articles have been reviewed after the screening process. The paper highlights a comparison between different algorithms used and their accuracy as well. It was observed that most researchers used DL algorithms to develop the ASD detection model. Different accuracies were recorded with a maximum accuracy close to 0.99. Recommendations for future work have also been discussed in a later section. This analysis derived a conclusion that AI-emerged DL and ML technologies can diagnose ASD through rs-fMRI images with maximum accuracy. The comparative analysis has been included to show the accuracy range.

Deception detection is a critical aspect across various domains. Integrating advanced signal processing techniques, particularly in neuroscientific studies, has opened new avenues for exploring deception at a deeper level. This study uses electroencephalogram (EEG) signals from a balanced cohort of 22 participants, consisting of both males and females, aged between 22 and 29, engaged in a visual task for instructed deception. We propose a novel approach in the realm of deception detection utilizing the Weighted Dual Perspective Visibility Graph (WDPVG) method to decode instructed deception by converting average epochs from each EEG channel into a complex network. Six graph-based features, including average and deviation of strength, weighted clustering coefficient, weighted clustering coefficient entropy, average weighted shortest path length, and modularity, are extracted, comprehensively representing the underlying brain dynamics associated with deception. Subsequently, these features are employed for classification using three distinct algorithms: K Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). Experimental results reveal promising accuracy rates for KNN (66.64%), SVM (86.25%), and DT (82.46%). Furthermore, the features distributions of EEG networks are analyzed across different brain lobes, comparing truth-telling and lying modes. These analyses reveal the frontal and parietal lobes’ potential in distinguishing between truth and deception, highlighting their active role during deceptive behavior. The findings demonstrate the WDPVG method’s effectiveness in decoding deception from EEG signals, offering insights into the neural basis of deceptive behavior. This research could enhance the understanding of neuroscience and deception detection, providing a framework for future real-world applications.

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 (({g}_{Na})) and membrane capacitance (({C}_{m})) weakens the XOR function, while regulation of potassium conductance (({g}_{K})) demonstrates robustness in maintaining the XOR function. Further analysis reveals that when a single pathway is activated, an increase in ({g}_{Na}) and ({C}_{m}) leads to decrease in the amplitude of dCaAPs, whereas increasing ({g}_{K}) 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.

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.

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.