Cognitive Neurodynamics最新文献

Vertigo, a prevalent neurovestibular disorder, arises from dysfunction in the vestibular system and often lacks precise, personalized treatments. This study proposes a bio-inspired spiking neural network (SNN) model that simulates vestibular dysfunction and adaptive recovery using Leaky Integrate-and-Fire (LIF) neurons with spike-timing-dependent plasticity (STDP). The architecture mimics the vestibular pathway through biologically plausible layers: hair cells, afferents, and cerebellar integrators, and models pathological states such as hair cell hypofunction and synaptic disruption. A reinforcement-based feedback mechanism enables the simulation of therapy-induced plasticity, resulting in a 48-62% drop and 38% recovery in cerebellar spike activity during adaptation epochs. The model demonstrates real-time feasibility, with an average simulation runtime of  4 s per epoch on standard hardware. Its design is scalable and well-suited for future deployment on neuromorphic platforms (e.g., Loihi, SpiNNaker). Its modular and interpretable design enables in silico testing of rehabilitation strategies, real-time monitoring of dysfunction, and future personalization using clinical datasets. This work establishes a computational foundation for AI-driven vestibular therapy that is adaptive, explainable, and hardware compatible.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10368-1.

Accurate localization of the seizure onset zone (SOZ) is critical for successful surgery in drug-resistant epilepsy (DRE). To investigate the alterations of network characteristics between the SOZ and non-seizure onset zones (NSOZ) across different seizure stages, the intracranial electroencephalogram (iEEG) data based brain networks from 29 DRE patients have been constructed using the weighted phase lag index (WPLI) and phase transfer entropy (PTE), respectively. Then, graph theory metrics, such as eigenvector centrality, betweenness centrality, in-degree and out-degree, are calculated to compare network characteristics of SOZ and NSOZ nodes across interictal, pre-ictal, early-ictal and post-ictal periods in multiple frequency bands. Statistical analyses demonstrate that the SOZ exhibits significantly higher eigenvector centrality and betweenness centrality in the beta and gamma frequency bands, serving as network hubs and primary sources of information outflow. By contrast, the NSOZ shows elevated centrality only in the theta and alpha frequency bands during non-ictal states. Moreover, during the pre-ictal to early-ictal transition, the SOZ progressively evolves into hub nodes with enhanced outflow and reduced inflow, whereas the NSOZ shifts toward non-hub status with increased inflow. Importantly, the random forest model utilizing out-degree features of early-ictal gamma frequency band can effectively identify the SOZ, and achieve an area under the curve (AUC) of 0.82. Overall, these findings offer a novel network-based perspective on the state-dependent alterations of epileptic seizures in DRE and contribute to the treatment of epilepsy.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10400-4.

Acupuncture modulates cognitive functions through acupoint stimulation and demonstrates significant regulatory effects on brain disorders. However, the underlying neurodynamic mechanisms of acupuncture remain unclear due to a lack of effective measures of brain activity. In this study, we developed an acupuncture-related potential (ARP) method based on Electroencephalogram (EEG) to elucidate the dynamic representation mechanisms underlying acupuncture stimulation. By analyzing ARP signal features and functional networks to capture stimulus-evoked brain activity, we derived spatiotemporal representations of neural manifolds and located across whole brain regions. It is exhibited that acupuncture induced significant four-phase event-related potentials (ERPs) waveforms predominantly in the parietal, frontal, central, and temporal lobes, with the parietal lobe exhibiting the highest amplitude at the P1 component (first positive peak). Latency gradients confirmed that the cortical neural activity originated in the parietal lobe and propagated through the central region to the frontal and temporal lobes. Dynamic network analysis revealed phase-specific reorganization: local frontal propagation (P1 component), global integration (P2 component), and novel topological pattern formation (P3 component). Neural manifold analysis uncovered a low-dimensional, ring-shaped representation encompassing the frontal, parietal, central, and temporal lobes. Acupuncture modulates brain function by activating key parietal lobe nodes, triggering distance-attenuated inter-regional signal transmission that dynamically reorganizes functional networks for multi-regional collaboration. The neural manifold representation revealed perception and integration of mechanisms of acupuncture information in the human brain. This ARP method provided a novel framework for investigating acupuncture-modulated spatiotemporal brain dynamics while enabling quantitative evaluation of its therapeutic effects.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10408-w.

Modern AI systems excel at pattern recognition and task execution, but they often fall short of replicating the layered, self-referential structure of human thought that unfolds over time. In this paper, we present a mathematically grounded and conceptually simple framework based on smoothed step functions-sigmoid approximations of Heaviside functions-to model the recursive development of mental activity. Each cognitive layer becomes active at a specific temporal threshold, with the abruptness or gradualness of activation governed by an impressiveness parameter [Formula: see text], which we interpret as a measure of emotional salience or situational impact. Small values of [Formula: see text] represent intense or traumatic experiences, producing sharp and impulsive responses, while large values correspond to persistent background stress, yielding slow but sustained cognitive activation. We formulate the recursive dynamics of these cognitive layers and demonstrate how they give rise to layered cognition, time-based attention, and adaptive memory reinforcement. Unlike conventional memory models, our approach captures thoughts and recall events through a recursive, impressiveness-sensitive pathway, leading to context-dependent memory traces. This recursive structure offers a new perspective on how awareness and memory evolve over time, and provides a promising foundation for designing artificial systems capable of simulating recursive, temporally grounded consciousness.

In biological neurons, synapses receive external stimuli to induce firing patterns. While the rapid generation of synapses regulates neural activity. In this paper, we use a magnetic-flux controlled memristor (MFCM) as a synapse to connect two functional neurons, establish the new coupled neurons, and study the synchronization characteristics. Firstly, we connect two neurons using memristive synapses, and derive the equations of the coupled neurons based on Kirchhoff's voltage law. Furthermore, we calculate the energy of the memristive coupling channels, and obtain the energy difference between the coupled neurons. Secondly, we propose a criterion for exponential growth controlled by energy difference. By setting higher coupling channel strength to establish synaptic connections, energy pumping can be effectively activated. Finally, for three modes, we analyze the energy evolution under the variations of memristive synapses, and find that the coupling channels are adaptively controlled by energy difference. The results show that when the coupling strength through synapses is enhanced, identical neurons can achieve complete synchronization, and different neurons can achieve phase locking. This study clarifies the underlying mechanisms of regulating coupled neurons via memristive synapses and explores how neurons achieve potential energy balance from the perspective of physical fields.

Functional differentiation in the brain emerges as distinct regions specialize and is key to understanding brain function as a complex system. Previous research has modeled this process using artificial neural networks with specific constraints. Here, we propose a novel approach that induces functional differentiation in recurrent neural networks by minimizing mutual information between neural subgroups via mutual information neural estimation. We apply our method to a 2-bit working memory task and a chaotic signal separation task involving Lorenz and Rössler time series. Analysis of network performance, correlation patterns, and weight matrices reveals that mutual information minimization yields high task performance alongside clear functional modularity and moderate structural modularity. Importantly, our results show that functional differentiation, which is measured through correlation structures, emerges earlier than structural modularity defined by synaptic weights. This suggests that functional specialization precedes and probably drives structural reorganization within developing neural networks. Our findings provide new insights into how information-theoretic principles may govern the emergence of specialized functions and modular structures during artificial and biological brain development.

With the development of emotion recognition technology in various applications, studies based on EEG signals were carried out as they can directly reflect brain activity. Although existing graph neural network (GNN) methods have made some progress in processing EEG signals, they still face significant limitations in capturing complex spatiotemporal dependencies, avoiding over-smoothing, and handling cross-regional brain signal interactions, which impact the accuracy and robustness of emotion recognition. To address these problems, this paper proposes a Hierarchical Multi-Scale Graph Neural Network (HMSGNN). This method enhances the spatiotemporal feature modeling ability of EEG signals by extracting features at multiple levels, from local to global, thus improving the accuracy and robustness of emotion recognition. Experimental results show that HMSGNN achieves recognition accuracies of 98.67% and 85.72% in subject-dependent experiments on the SEED and SEED-IV datasets, and 87.11% and 76.14% in subject-independent experiments, respectively. Under the reproduced experimental settings, these values are the highest among the compared methods, while maintaining comparable or lower variance.

The processing of information within complex neural networks is a challenge topic that has intrigued researchers for many years. In this paper, we conducted an in-depth investigation into the learning mechanisms that are intrinsic to discrete memristor spiking neural networks. We also explored the effectiveness of information transmission and synchronization among various neurons and networks. Firstly, a memristor model with memory regulation function and tanh function's nonlinear characteristics was constructed. This model not only ensures that the internal state variables of the memristor do not exhibit divergence, but also demonstrates that this memristor is suitable for spiking signal processing and has the ability to transmit spiking signals. Secondly, our research delved into the intricate dynamics of these discrete spiking neural networks, including the ternary coupled spiking neural network and ring coupled spiking neural network, aiming to shed light on how they operate and interact. Thirdly, based on the designed pulse neurons, this study constructed a simple pulse neuron network. By reasonably setting the relevant parameters, the research found that this network possesses the ability for pattern recognition. The results of our investigation are crucial for understanding the mechanisms of information processing and synchronization phenomena within neural networks. It provides valuable insights into the potential of memristor networks in advancing artificial intelligence and computational neuroscience.

Fast-spiking basket cells (FSBCs) govern hippocampal oscillations through their rapid and sustained firing patterns, which drive rhythmic inhibition onto postsynaptic neurons, thereby enforcing population synchrony in the gamma and other frequency bands that support cognitive processes. Despite the established role of FSBCs in hippocampal oscillations, the precise mechanisms by which their dendrites influence membrane potential responses across different frequency bands remain unclear. In this study, we simulate oscillation-like input protocols to explore how dendrites modulate the spectral responses of the membrane potentials of FSBCs. Our results show that FSBCs exhibit both slow and fast oscillatory components, which are shaped by their action potentials. Input synchrony is essential for determining both the fast-band response frequency and its coupling with the slow frequency, while the neuron's intrinsic firing dynamics maintain the stability of the fast-band peak frequency across theta-range inputs. Although dendritic Na[Formula: see text]/A-type K[Formula: see text] channel blockade and cp-AMPA enhancement both increase fast-band frequency, they differentially affect phase-amplitude coupling, with blockade reducing and cp-AMPA enhancement increasing it, highlighting the role of intrinsic dendritic conductances and cp-AMPA inputs in promoting coupling. Furthermore, we show that the spatial distribution of synaptic inputs along dendrites affects the response frequencies, with distinct frequencies observed at different dendritic locations according to their electrotonic distance. These findings provide insights into how the intrinsic properties of FSBCs influence their response to oscillatory inputs.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-026-10418-2.

Intrusive saccades during active visual fixation indicate deficits in inhibitory control which is crucial for cognitive control function. Research has shown that abnormalities in these mechanisms are linked to neurological disorders such as schizophrenia and obsessive-compulsive disorder (OCD), both involving dysfunctions in frontal-subcortical circuits. Eye movement studies and machine learning (ML) techniques have been used to differentiate clinical from neurotypical populations. This study aimed to classify healthy controls, patients with OCD and schizophrenia patients, based on oculomotor behavior during active fixation tasks and provide insights into related neurophysiological mechanisms. Data from three visual fixation tasks were analyzed using statistical tests to select saccade features to be used in the classification. A shallow Artificial Neural Network (ANN) was implemented for binary and three-class classification. Binary classification achieved 87% accuracy and 93% specificity in distinguishing controls from the patients with schizophrenia group, 84% accuracy and 90% sensitivity in distinguishing between controls and medicated patients with OCD not taking antipsychotics, while differentiation between patients with schizophrenia and medicated patients with OCD not taking antipsychotics reached 77% accuracy and 82% specificity. The findings provided indications that selected saccadic features can differentiate OCD and schizophrenia patients from healthy controls using shallow ANNs, while distinguishing between OCD and schizophrenia patients remains more challenging. Notably, tentative indications were provided that group differences were driven more by intrinsic saccadic generation properties than by fixation or inhibitory mechanisms, concerning unwanted saccades that are intrusive in nature in the context of fixation.