Cognitive Neurodynamics最新文献

Methamphetamine (MA) abuse is increasing worldwide, and evidence indicates that MA causes degraded cognitive functions such as executive function, attention, and flexibility. Recent studies have shown that regular physical exercise can ameliorate the disturbed functions. However, the potential functional network alterations resulting from physical exercise have not been extensively studied in female MA users. We collaborated with a drug rehabilitation center for this study to investigate changes in brain activity and network dynamics after two types of acute and long-term exercise interventions based on 64-channel electroencephalogram recordings of seventy-nine female MA users, who were randomly divided into three groups: control group (CG), dancing group (DG) and bicycling group (BG). Over a 12-week period, we observed a clear drop in the rate of brain activity in the exercise groups, especially in the frontal and temporal regions in the DG and the frontal and occipital regions in the BG, indicating that exercise might suppress hyperactivity and that different exercise types have distinct impacts on brain networks. Importantly, both exercise groups demonstrated enhancements in brain flexibility and network connectivity entropy, particularly after the acute intervention. Besides, a significantly negative correlation was found between Δattentional bias and Δbrain flexibility after acute intervention in both DG and BG. Analysis strongly suggested that exercise programs can reshape patient brains into a highly energy-efficient state with a lower activity rate but higher information communication capacity and more plasticity for potential cognitive functions. These results may shed light on the potential therapeutic effects of exercise interventions for MA users.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-022-09848-5.

Data-driven strategies have been widely used to distinguish experimental effects on single-trial EEG signals. However, how latency variability, such as within-condition jitter or latency shifts between conditions, affects the performance of EEG classifiers has not been well investigated. Without explicitly considering and disentangling such attributes of single trials, neural network-based classifiers have limitations in measuring their contributions. Inspired by domain knowledge of subcomponent latency and amplitude from traditional cognitive neuroscience, this study applies a stepwise latency correction method on single trials to control for their contributions to classifier behavior. As a case study demonstrating the value of this method, we measure repetition priming effects of faces, which induce large reaction time differences, latency shifts, and amplitude effects in averaged event-related potentials. The results show that within-condition jitter negatively impacts classifier performance, but between-condition latency shifts improve accuracy, whereas genuine amplitude differences have no significant influence. While demonstrated in the case of priming effects, this methodology can be generalized to experiments involving many kinds of time-varying signals to account for the contributions of latency variability to classifier performance.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-024-10181-2.

Finding the synchronization between Electroencephalography (EEG) and human cognition is an essential aspect of cognitive neuroscience. Adaptive Control of Thought-Rational (ACT-R) is a widely used cognitive architecture that defines the cognitive and perceptual operations of the human mind. This study combines the ACT-R and EEG-based cortex-level connectivity to highlight the relationship between ACT-R modules during the EEG-based n-back task (for validating working memory performance). Initially, the source localization method is performed on the EEG signal, and the mapping between ACT-R modules and corresponding brain scouts (on the cortex surface) is performed. Once the brain scouts are identified for ACT-R modules, then those scouts are called ACT-R scouts. The linear (Granger Causality: GC) and non-linear effective connectivity (Multivariate Transfer Entropy: MTE) methods are applied over the scouts' time series data. From the GC and MTE analysis, for all n-back tasks, information flow is observed from the visual-to-imaginal ACT-R scout for storing the visual stimuli (i.e., input letter) in short-term memory. For 2 and 3-back tasks, causal flow exists from imaginal to retrieval ACT-R scout and vice-versa. Causal flow from procedural to the imaginal ACT-R scout is also observed for all workload levels to execute the set of productions. Identifying the relationship among ACT-R modules through scout-level connectivity in the cortical surface facilitates the effects of human cognition in terms of brain dynamics.

[This corrects the article DOI: 10.1007/s11571-023-10011-x.].

From the dynamical point of view, most cognitive phenomena are hierarchical, transient and sequential. Such cognitive spatio-temporal processes can be represented by a set of sequential metastable dynamical states together with their associated transitions: The state is quasi-stationary close to one metastable state before a rapid transition to another state. Hence, we postulate that metastable states are the central players in cognitive information processing. Based on the analogy of quasiparticles as elementary units in physics, we introduce here the quantum of cognitive information dynamics, which we term "cognon". A cognon, or dynamical unit of thought, is represented by a robust finite chain of metastable neural states. Cognons can be organized at multiple hierarchical levels and coordinate complex cognitive information representations. Since a cognon is an abstract conceptualization, we link this abstraction to brain sequential dynamics that can be measured using common modalities and argue that cognons and brain rhythms form binding spatiotemporal complexes to keep simultaneous dynamical information which relate the 'what', 'where' and 'when'.

Theories of the electrophysiology of language comprehension are mostly informed by event-related potential effects observed between condition averages. We here argue that a dissociation between competing effect-level explanations of event-related potentials can be achieved by turning to predictions and analyses at the single-trial level. Specifically, we examine the single-trial dynamics in event-related potential data that exhibited a biphasic N400-P600 effect pattern. A group of multi-stream models can explain biphasic effects by positing that each individual trial should induce either an N400 increase or a P600 increase, but not both. An alternative, single-stream account, Retrieval-Integration theory, explicitly predicts that N400 amplitude and P600 amplitude should be correlated at the single-trial level. In order to investigate the single-trial dynamics of the N400 and the P600, we apply a regression-based technique in which we quantify the extent to which N400 amplitudes are predictive of the electroencephalogram in the P600 time window. Our findings suggest that, indeed, N400 amplitudes and P600 amplitudes are inversely correlated within-trial and, hence, the N400 effect and the P600 effect in biphasic data are driven by the same trials. Critically, we demonstrate that this finding also extends to data which exhibited only monophasic effects between conditions. In sum, the observation that the N400 is inversely correlated with the P600 on a by-trial basis supports a single stream view, such as Retrieval-Integration theory, and is difficult to reconcile with the processing mechanisms proposed by multi-stream models.

This study delves into the examination of a network of adaptive synapse neurons characterized by a small-world network topology connected through electromagnetic flux and infused with randomness. First, this research extensively explores the existence of the global multi-stability of a single adaptive synapse-based neuron model with magnetic flux. The non-autonomous neuron model exhibits periodically switchable equilibrium states that are strongly related to the transitions between stable and unstable points in every whole periodic cycle, leading to the creation of global multi-stability. Various numerical measures, including bifurcation plots, phase plots, and basin of attraction, illustrate the intricate dynamics of diverse coexisting global firing activities. Moreover, the model is extended by coupling two neurons with a memristive synapse. The dynamics of the coupled neurons model are showcased with the help of largest Lyapunov exponents, and synchronized dynamics are viewed with the help of mean average error. Next, we consider a regular network of neurons connected to their nearest neighbors through the memristive synapse. We then reconstruct it into a small-world network by increasing the randomness in the rewiring links. Consequently, we observed collective behavior influenced by the number of neighborhood connections, coupling strength, and rewiring probability. We used spatio-temporal patterns, recurrence plots, as well as global-order parameters to verify the reported results.

The integration and interaction of cross-modal senses in brain neural networks can facilitate high-level cognitive functionalities. In this work, we proposed a bioinspired multisensory integration neural network (MINN) that integrates visual and audio senses for recognizing multimodal information across different sensory modalities. This deep learning-based model incorporates a cascading framework of parallel convolutional neural networks (CNNs) for extracting intrinsic features from visual and audio inputs, and a recurrent neural network (RNN) for multimodal information integration and interaction. The network was trained using synthetic training data generated for digital recognition tasks. It was revealed that the spatial and temporal features extracted from visual and audio inputs by CNNs were encoded in subspaces orthogonal with each other. In integration epoch, network state evolved along quasi-rotation-symmetric trajectories and a structural manifold with stable attractors was formed in RNN, supporting accurate cross-modal recognition. We further evaluated the robustness of the MINN algorithm with noisy inputs and asynchronous digital inputs. Experimental results demonstrated the superior performance of MINN for flexible integration and accurate recognition of multisensory information with distinct sense properties. The present results provide insights into the computational principles governing multisensory integration and a comprehensive neural network model for brain-inspired intelligence.

In this study, we investigate the impact of first and second-order coupling strengths on the stability of a synchronization manifold in a Discrete FitzHugh-Nagumo (DFHN) neuron model with memristor coupling. Master Stability Function (MSF) is used to estimate the stability of the synchronized manifold. The MSF of the DFHN model exhibits two zero crossings as we vary the coupling strengths, which is categorized as class ${\Gamma }_2$ . Interestingly, both zero-crossing points demonstrate a power-law relationship with respect to both the first-order coupling strength and flux coefficient, as well as the second-order coupling strength and flux coefficient. In contrast, the zero crossings follow a linear relationship between first-order and second-order coupling strength. These linear and nonlinear relationships enable us to forecast the zero-crossing point and, consequently, determine the coupling strengths at which the stability of the synchronization manifold changes for any given set of parameters. We further explore the regime of the stable synchronization manifold within a defined parameter space. Lower values of both first and second-order coupling strengths have minimal impact on the transition between stable and unstable synchronization regimes. Conversely, higher coupling strengths lead to a shrinking regime of the stable synchronization manifold. This reduction follows an exponential relationship with the coupling strengths. This study is helpful in brain-inspired computing systems by understanding synchronization stability in neuron models with memristor coupling. It helps to create more efficient neural networks for tasks like pattern recognition and data processing.

Adaptive mechanisms of learning models play critical roles in interpreting adaptive behavior of humans and animals. Different learning models, varying from Bayesian models, deep learning or regression models to reward-based reinforcement learning models, adopt similar update rules. These update rules can be reduced to the same generalized mathematical form: the Rescorla-Wagner equation. In this paper, we construct a hierarchical Bayesian model with an adaptive learning rate for inferring a hidden probability in a dynamical binary environment, and analysis the adaptive behavior of the model on synthetic data. The update rule of the model state turns out to be an extension of the Rescorla-Wagner equation. The adaptive learning rate is modulated by beliefs and environment uncertainty. Our results underscore adaptive learning rate as mechanistic component in efficient and accurate inference, as well as the signature of information processing in adaptive machine learning models.