Cogn Neurodyn [JOURNAL]
Sun
200 papers
Olfactory Perception and Neural Rhythms: A Simulation-Based EEG Analysis Using Power Spectral Density FeaturesOlfactory perception and neural rhythms: a simulation-based eeg analysis using power spectral density features.
S V A
Cogn Neurodyn · 2026 Dec · PMID 42389048 · Full text
The human ability to smell functions as a critical cognitive function because it enables people to detect their surroundings while experiencing feelings and recalling memories and making choices. Researchers face difficu...
The human ability to smell functions as a critical cognitive function because it enables people to detect their surroundings while experiencing feelings and recalling memories and making choices. Researchers face difficulties when they use electroencephalography (EEG) to study how the brain responds to smells because olfactory brain signals produce low signal-to-noise ratios and different people show different response patterns and researchers lack established olfactory EEG databases for their studies. The study proposes a simulation-based framework which enables researchers to study olfactory EEG signals through power spectral density (PSD) analysis. The research team created a simulated olfactory EEG dataset which simulated the responses of fifty virtual participants who experienced two distinct odor categories of pleasant rose and unpleasant rotten at three different concentration levels of low medium and high to create six separate olfactory conditions. The simulated EEG signals included 45 channels which recorded data at a 256 Hz sampling rate. Welch's method estimated PSD features for five canonical EEG frequency bands which included delta theta alpha beta and gamma after the data underwent band-pass filtering at the 0.5-70 Hz range. The researchers used Stratified 10-fold cross-validation to evaluate the band's characteristics which they had developed as training data for their multiclass support vector machine (SVM) classification model. The PSD-based features demonstrated their ability to distinguish between different olfactory conditions in controlled tests which showed the system's classification accuracy of 99.67% and macro-averaged F1-score of 0.99. The research provides a methodological validation platform which enables scientists to conduct reproducible olfactory EEG studies through their complete pipeline of interpretation. The proposed framework establishes the essential foundations for subsequent research which will assess and develop these techniques through actual human olfactory EEG data in cognitive neuroscience studies.
An event-related potentials account of brain predictive coding.
Gómez CM, Arjona A, Ruíz-Martínez FJ … +3 more , Muñoz-Caracuel M, Muñoz V, Rodriguez-Martinez EI
Cogn Neurodyn · 2026 Dec · PMID 42389047 · Full text
Predictive coding is a theory that tries to account for how the brain processes in an anticipatory manner the expected stimuli, and reorganizes the underlying neural networks as a consequence of the outcome of prediction...
Predictive coding is a theory that tries to account for how the brain processes in an anticipatory manner the expected stimuli, and reorganizes the underlying neural networks as a consequence of the outcome of predictions: Correct or incorrect. EEG has the advantage of making a continuous and almost instantaneous record of brain activity. The present report summarizes work on Event-Related Potentials (ERPs) and reviews the neural validity of Predictive processing as a mechanism to predict future events, assess the validity of predictions, and then update the probabilities associated with future events. Using two experimental models: predictive tone sequences and central cue Posner paradigms and Bayesian modelling, the report suggests that Contingent Negative Variation (CNV) would be related to prior expectation, Mismatch negativity (MMN) and P300 to Bayesian surprise and/or prediction error, and Post Imperative Negative Variation (PINV) to the assessment of trial outcome in uncertainty situations. The review tends to support predictive coding as a theory consistent with brain operations indexed by ERPs.
A recurrent neural network model for a decision-making task based on sequential evidence accumulation.
Liu J, Wang Y, Xu X … +1 more , Pan X
Cogn Neurodyn · 2026 Dec · PMID 42389046 · Full text
UNLABELLED: Decision making typically entails the accumulation of evidence, a process supported by coordinated neural activity across multiple brain regions. Numerous studies have shown that the dorsolateral prefrontal c...
UNLABELLED: Decision making typically entails the accumulation of evidence, a process supported by coordinated neural activity across multiple brain regions. Numerous studies have shown that the dorsolateral prefrontal cortex (DLPFC) participates in evidence accumulation for decision-making. Single neurons in the DLPFC display ramping responses related to accumulated evidence, and neuronal populations stably encode the integrated evidence that supports the transformation from sensory inputs to actions. Previous studies have proposed computational models of evidence accumulation in decision-making. However, it is still unclear how serial information can be dynamically integrated across multiple timescales and how the stability of accumulated states is maintained. To address these issues, we proposed a recurrent neural network (RNN) model with reinforcement learning to probe the neural computations underlying evidence accumulation in the decision-making task. Simulation results show that the model successfully learned to perform the evidence accumulation decision-making task. The population activity of recurrent units shows distinct coding patterns: one subset displayed transient responses to instantaneous evidence under different stimulus conditions; another subset exhibited activity that gradually increased or decreased with the amount of accumulated evidence in favor of the preferred target, thereby reflecting the process of evidence accumulation. Further analyses indicated that the network did not simply track instantaneous evidence, but rather tended to integrate these signals over time to complete the decision. This property resembles the behavior of DLPFC neurons in similar tasks, and highlights the model's capacity for dynamic integration of evidence. Furthermore, we found that both suppressing the activity of specific units and disrupting network connections impaired the model's decision-making performance, thereby validating the critical role of this network architecture in executing the task. Taken together, the simulated results suggest that the model accumulates sequentially inputted evidence for decision-making and offers a possible computational way to understand how evidence is accumulated in the neural level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10484-6.
Synaptic neurotransmitter concentration modulation during learning in bio-inspired spiking neural network.
Yadav S, Gehlot N, Chaudhary S … +2 more , Kumar R, Nkomozepi P
Cogn Neurodyn · 2026 Dec · PMID 42389045 · Full text
Establishing concentration profiles of neurotransmitters during learning is an important step toward understanding the basic properties of communication between neurons. A variety of attempts have been made by researcher...
Establishing concentration profiles of neurotransmitters during learning is an important step toward understanding the basic properties of communication between neurons. A variety of attempts have been made by researchers in various fields, such as neuroscience, pharmacology, toxicology, immunology, and psychology, to find the neurotransmitter concentration in various scenarios, such as synaptic transmission, homeostasis, and psychiatric conditions. However, due to the complex structure of the brain, the general method based on the concentration of neurotransmitters in continuous, large-scale, and simultaneous measurement across multiple neurotransmitters during learning remains challenging. Inspired by that, this study proposes a new computational model that uses consciousness-driven plasticity metrics along with various factors like release rate, reuptake rate, and degradation rate to analyze the neurotransmitter concentration during learning using a spiking neural network. The simulated neurotransmitter concentrations (in milliMolar) for the MNIST and Fashion-MNIST datasets on the proposed model are [0.12, 0.18] and [0.0, 0.04] for dopamine, [0.0, 0.16] and [0.28, 0.46] for norepinephrine, [0.0, 0.10] and [0.29, 0.39] for acetylcholine, [0.0, 0.12] and [0.20, 0.33] for serotonin, [0.02, 0.27] and [0.73, 1.11] for glutamate, and [4.81, 7.19] and [5.19, 7.35] for GABA, respectively. These values closely align with biological values. Also, the quantitative correlation analysis is being performed for the model to provide biological alignment of the model.
A two-neuron HETUF-memristive hopfield neural network and its application in image encryption.
Li Z, Yang L, Jin J … +1 more , Yu F
Cogn Neurodyn · 2026 Dec · PMID 42389044 · Full text
Memristive Hopfield neural networks (MHNN) integrate the non-volatile memory of memristors and feedback dynamics of neural networks, showing great potential in chaotic information security. Nevertheless, most existing MH...
Memristive Hopfield neural networks (MHNN) integrate the non-volatile memory of memristors and feedback dynamics of neural networks, showing great potential in chaotic information security. Nevertheless, most existing MHNNs adopt the conventional [Formula: see text] activation function, which suffers from vanishing gradients and low dynamical complexity. Besides, studies on activation function optimization for Hopfield neural networks (HNN) are still insufficient. Therefore, a novel hybrid exponential Tanh unit function (HETUF) activation function is proposed in this work. Based on the HETUF activation function, a compact two-neuron HETUF-memristive Hopfield neural network (HETUF-MHNN) is constructed. Dynamical analyses and quantitative comparisons with the tanh-MHNN are conducted by using phase portraits, 0-1 test, Lyapunov exponents and bifurcation diagrams. A new phenomenon named derived chaotic decay with time-dependent attenuation is discovered, and its essential difference from traditional multi-stability is clarified. Additionally, a coordinate-constrained attractor capturing strategy is presented, and the corresponding chaotic sequences also pass the NIST test. Finally, a low-complexity spatial domain HETUF-MHNN-based image encryption scheme is designed and validated.
MEK-ERK inhibition enhances synaptic input-output coupling and neuronal excitability in the rat dentate gyrus: association with site-specific Kv4.2 phosphorylation.
Koşar B, Yaylalı O, Baş D … +3 more , Çimen A, Dursun N, Süer C
Cogn Neurodyn · 2026 Dec · PMID 42389043 · Full text
Synaptic plasticity in the dentate gyrus requires a balance between synaptic transmission and excitability. Although the mitogen-activated protein kinase kinase (MEK)-extracellular signal-regulated kinase (ERK) signaling...
Synaptic plasticity in the dentate gyrus requires a balance between synaptic transmission and excitability. Although the mitogen-activated protein kinase kinase (MEK)-extracellular signal-regulated kinase (ERK) signaling pathway is well known for its role in long-term plasticity, its function in the regulation of excitatory postsynaptic potential-to-spike (E-S) coupling remains comparatively underexplored. In this study, 40 adult male Wistar albino rats were used to investigate the effects of MEK-ERK inhibition on changes in synaptic transmission, neuronal excitability, and Kv4.2 potassium channel phosphorylation following low-frequency stimulation (LFS) in the in vivo hippocampus. Field potentials were recorded from the dentate gyrus in response to medial perforant pathway stimulation. Inhibition of MEK with PD98059 did not alter basal field excitatory postsynaptic potential (fEPSP) slopes but significantly increased population spike (PS) amplitudes. Under LFS, MEK-ERK inhibition paradoxically increased neuronal output while decreasing excitatory synaptic input, indicating enhanced E-S coupling between synaptic input and output. Western blot analyses confirmed reduced ERK phosphorylation without changes in total ERK levels after PD98059 application. Despite decreased ERK activity, phosphorylation of Kv4.2 increased at specific sites: Thr602 was elevated selectively under LFS, whereas Thr607 increased independently of stimulation. A significant main effect of decreased total Kv4.2 protein levels was also observed. These findings indicate that the MEK-ERK pathway differentially modulates synaptic transmission and neuronal excitability in the dentate gyrus. Site-specific Kv4.2 phosphorylation at Thr602/Thr607, together with reduced total Kv4.2 expression, may contribute to the observed E-S potentiation. This mechanism may underlie hyperexcitability associated with neurological disorders.
HF-SNVTA-FusionNet: high-frequency multi-domain EEG feature fusion from the substantia nigra and ventral tegmental area for Parkinson's disease classification.
Kumar K, Abhaya
Cogn Neurodyn · 2026 Dec · PMID 42368834 · Full text
Parkinson's disease (PD) is a progressive neurodegenerative disorder that severely affects motor and cognitive functions, making early and accurate diagnosis crucial for effective clinical management. This research intro...
Parkinson's disease (PD) is a progressive neurodegenerative disorder that severely affects motor and cognitive functions, making early and accurate diagnosis crucial for effective clinical management. This research introduces the high-frequency substantia nigra and ventral tegmental area fusion network (HF-SNVTA-FusionNet), a robust EEG-based PD detection framework. The system employs independent component analysis (ICA) for artifact removal, multi-domain feature extraction (time, frequency, and time-frequency), and principal component analysis (PCA) for dimensionality reduction, followed by a CNN-BiGRU-multi-head self-attention (MHSA) classification pipeline. Specifically, CNN captures local spatial patterns, BiGRU models bidirectional temporal dependencies, and MHSA refines salient features, enabling improved discrimination between PD and healthy EEG signals. Experiments on three benchmark datasets (UI, PDG, and USDRS) demonstrate superior performance, achieving accuracies of 100%, 98.89%, and 98.15% within the 50-70 Hz band using 64 PCA features. Comparative evaluation confirms its advantage over existing state-of-the-art models. The proposed system holds strong potential for real-world PD screening and can be extended to cognitive task-based EEG analysis and low-power hardware deployment.
Investigation of the effects of balance exercises on visuospatial skills using EEG brain oscillations.
Yıldırım Y, Yemeniciler İ, Tarakcı D … +1 more , Güntekin B
Cogn Neurodyn · 2026 Dec · PMID 42368833 · Full text
UNLABELLED: Visual-spatial attention (VSA) selects relevant sensory information and supports the preparation of responses to this information. Mental rotation (MR) is the ability to rotate an object seen from a certain p...
UNLABELLED: Visual-spatial attention (VSA) selects relevant sensory information and supports the preparation of responses to this information. Mental rotation (MR) is the ability to rotate an object seen from a certain perspective to a new orientation in space. Exercise stands out as a promising non-pharmacological treatment for cognitive functions. Balance control is known to be related to the visual system. Therefore, the aim was to investigate the effectiveness of video-based balance games and structured balance exercises on VSA and MR with EEG brain oscillations. 30 healthy participants were included in the study. Participants were divided into two groups (structured balance exercises group (SBEG) and video-based balance exercises group (VBBEG)) by randomization. Both groups received exercise sessions 2 days a week for a total of 6 weeks. The mentioned cognitive functions were evaluated by selecting tests previously used in the literature. For the VSA task, after 6 weeks of exercise, occipital theta (4-7 Hz) power decreased in the VBBEG group, while SBEG increased. In the MR task results, high alpha (11-13 Hz) power decreased in VBBEG and increased in SBEG when centroparietal areas were examined. In conclusion, it is thought that the two different exercise methods may affect visual-spatial attention and mental rotation skills in different ways. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10494-4.
MSCANet: a cross-attention-based multi-scale convolutional fusion neural network for EEG motor imagery classification.
Qin G, Huang J, Mi P … +2 more , Liu Z, Yang Y
Cogn Neurodyn · 2026 Dec · PMID 42368832 · Full text
UNLABELLED: Brain-computer interfaces (BCIs) utilize physiological neural signals from the brain to control external devices, offering significant potential to restore normal life in patients with brain injuries or motor...
UNLABELLED: Brain-computer interfaces (BCIs) utilize physiological neural signals from the brain to control external devices, offering significant potential to restore normal life in patients with brain injuries or motor impairments. However, electroencephalogram (EEG) signals are inherently non-stationary, possess low signal-to-noise ratios, and exhibit inter-subject variability, posing substantial decoding challenges. To effectively integrate multi-scale spatiotemporal features, this study proposes a cross-attention-based multi-scale convolutional fusion neural network (MSCANet) that integrates local and global features while capturing temporal dependencies across multiple scales. Specifically, MSCANet first employs a multi-scale spatio-temporal convolutional module to extract localized spatio-temporal information from variable-sized windows within individual frequency bands. Subsequently, channel and spatial attention mechanisms are incorporated to enhance discriminative feature representation by prioritizing salient information. A temporal convolution module with multi-level residual connections then preliminarily captures both short- and long-term dependencies. Finally, cross-attention mechanisms further capture temporal correlations and fuse features across frequency bands before classification via fully connected layers. In subject-dependent experiments, MSCANet achieved classification accuracies of 82.06% and 87.45%, with kappa values of 0.76 and 0.76 on the BCI IV-2a and BCI IV-2b Datasets, respectively. The proposed method outperforms several comparative models and demonstrates promising potential for BCI applications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10485-5.
Regulation of epileptiform discharges in thalamocortical model based on preview control theory.
Wang Z, Yang J, Duan L
Cogn Neurodyn · 2026 Dec · PMID 42273274 · Full text
Based on the important function of the thalamic reticular nucleus(TRN) in epileptic seizures, we explore how changes in TRN information affect epileptic seizures across the entire brain nervous system by adjusting the ex...
Based on the important function of the thalamic reticular nucleus(TRN) in epileptic seizures, we explore how changes in TRN information affect epileptic seizures across the entire brain nervous system by adjusting the excitatory coupling strength of excitatory pyramidal neuron(PY) and specific relay nucleus(SRN) to TRN. Through single-parameter and double-parameter bifurcation analyses, we derive that Hopf bifurcation and limit point cycle bifurcation are the critical elements triggering the system's state transitions, which subsequently gives rise to the tristable region. We design a preview controller to suppress epileptic seizures within the tristable region and investigate the control effect on epileptic seizures under the initial states of SWD and 2-SWD respectively. The results show that only PY has a significant effect when we apply the controller to a single nucleus. Based on this, we choose the controller to act on multiple nuclei containing PY simultaneously. Through numerical analysis, we find that the control effect is optimal when the controller is simultaneously applied to the PY, inhibitory interneuron(IN), and excitatory interneuron(EIN) populations. The preview controller performs better in suppressing seizures and responding to secondary disturbances when applied to multiple populations that include PY, whereas its advantage is not significant for single nucleus control acting solely on non-PY populations. This provides a new theoretical perspective for the design of closed-loop neuromodulation strategies.
Computational modeling of tyrosine hydroxylase pathway for dopamine synthesis in nerve cells: effect of tetrahydrobiopterin deficiency and oxidative stress.
Damdoum M, Kar S, Kavdia M
Cogn Neurodyn · 2026 Dec · PMID 42266201 · Full text
UNLABELLED: In neuronal dysfunction, there is a disruption of synthesis and release of neurotransmitters including dopamine. Oxidative stress is a major contributor to neuronal dysfunction. Oxidative stress affects tetra...
UNLABELLED: In neuronal dysfunction, there is a disruption of synthesis and release of neurotransmitters including dopamine. Oxidative stress is a major contributor to neuronal dysfunction. Oxidative stress affects tetrahydrobiopterin (BH), an essential cofactor in the synthesis of several neurotransmitters such as dopamine and nitric oxide. BH supports the activity of enzyme tyrosine hydroxylase (TH) and initiates dopamine synthesis. Studies have reported contradictory results on neuronal dysfunction and disease progression following treatments to reduce oxidative stress and BH supplementation. In this study, we developed a computational model of TH biochemical pathway in dopaminergic nerve cells. Using this model, we quantitatively analyzed the impact of reduced BH synthesis and oxidative stress on L-dopa and dopamine synthesis. The base case concentration of L-dopa was 382.5 nM and cytosolic dopamine was 12.4 nM. The results showed a significant decrease in the concentrations of L-dopa and dopamine with reduction in BH synthesis. The presence of oxidative stress further exacerbated these decreases in concentration. The mechanistic analysis suggests that this computational model provides an initial framework for evaluating how BH-related perturbations influence species concentrations in the TH biochemical pathway in dopaminergic nerve cells. Our findings indicate that under the simulated conditions, BH supplementation produced short-term changes in species concentrations and that sustained improvement in nerve cell dysfunction likely requires a multi-target approach that simultaneously enhances de novo BH synthesis and reduces cellular oxidative stress. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10486-4.
From nonlinear neuronal dynamics to AI-optimized VLSI hardware: multiplier-free FPGA implementation of memristive FN-HR coupled neural networks for intelligent systems.
Wu W, Yu W, Wang C … +5 more , Daoud MS, Mayet AM, Ge Y, Pan X, Zhang G
Cogn Neurodyn · 2026 Dec · PMID 42266200 · Full text
This paper proposes an innovative approach to translating the nonlinear dynamics of a memristive FitzHugh-Nagumo-Hindmarsh-Rose (FN-HR) coupled neuron model into an AI-optimized, resource-efficient VLSI implementation on...
This paper proposes an innovative approach to translating the nonlinear dynamics of a memristive FitzHugh-Nagumo-Hindmarsh-Rose (FN-HR) coupled neuron model into an AI-optimized, resource-efficient VLSI implementation on FPGA platforms, advancing intelligent computing paradigms. The bidirectional memristive synapse coupling FN and HR neurons enables rich dynamic behaviors such as mixed-mode oscillations and chaos, which are harnessed to enhance adaptive machine learning and neural network training. A detailed dynamical analysis, including Lyapunov exponent spectra and synchronization properties, identifies parameter regimes suitable for AI applications. Nonlinear operators are approximated using quantized lookup tables and three-term sinusoidal expansions, achieving RMSE values of 0.0105 (FN) and 0.0114 (HR) while eliminating DSP usage. Synthesized on an AMD Zynq UltraScale+ ZCU104 FPGA, a 50-neuron network utilizes 5.3% LUTs and 7% BRAM, delivering 42 million neuron-updates per second at 210 mW. This work establishes a scalable, low-power platform for real-time AI-driven neuromorphic computing and intelligent adaptive control systems.
Social communication and brain network properties: cognitive predictors and modulation by autistic traits.
Valles-Capetillo E, Angeles-Valdez D, Giordano M … +1 more , Kana RK
Cogn Neurodyn · 2026 Dec · PMID 42266199 · Full text
UNLABELLED: Social communication (SC) relies on the integration of linguistic processing, pragmatic inference, and theory of mind (ToM), yet its neural architecture remains insufficiently characterized. We propose and em...
UNLABELLED: Social communication (SC) relies on the integration of linguistic processing, pragmatic inference, and theory of mind (ToM), yet its neural architecture remains insufficiently characterized. We propose and empirically test a unified SC network by integrating information neural graph theory metrics, multi-domain cognitive modeling, and autistic traits. Forty-five neurotypical adults completed a cognitive battery assessing language, executive functions, social cognition, perceptual reasoning, and the autism spectrum quotient (AQ). Global efficiency, local efficiency, and clustering coefficients were computed for language, pragmatic, and ToM networks, and their combined architecture. A replication analysis for graph-metrics and its relationship with AQ was performed using an independent sample (n = 73, 31 autistic). Results revealed that language abilities were the strongest and most consistent predictors of network efficiency, particularly within temporal-parietal nodes implicated in semantic integration and contextual interpretation. Executive functions selectively predicted efficiency within frontal control regions, while perceptual reasoning was associated with global efficiency of the precuneus, associated with social and inferential processing. Importantly, autistic traits moderated multiple brain-behavior relationships, indicating that trait-level variability shapes how cognitive abilities map onto neural efficiency within neurotypical population. The replication analysis showed partial overlap with graph-metric and AQ results. These findings advance a network-level account of SC, demonstrating that communicative competence emerges from dynamic interactions among linguistic, executive, and inferential systems, whose neural organization is tuned by individual cognitive profiles and autistic traits. This dimensional framework provides a foundation for understanding variability in social-cognitive functioning and has implications for personalized models of communication. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10481-9.
Memristor-based neuromorphic circuit for visual emotional non-associative learning.
Wang Y, Shao S, Wang Y … +1 more , Sun J
Cogn Neurodyn · 2026 Dec · PMID 42266198 · Full text
Most of the nonassociative learning neural networks based on memristor only consider habituation or sensitization and ignore the combination with emotions. According to biological characteristics, a nonassociative learni...
Most of the nonassociative learning neural networks based on memristor only consider habituation or sensitization and ignore the combination with emotions. According to biological characteristics, a nonassociative learning memristive neural network circuit of emotions induced by vision is designed in this paper. The circuit comprises the voltage control module, the synapse module, the potentiation of habituation module and the spontaneous recovery module. The functions such as habituation and spontaneous recovery, habituation of dishabituation, potentiation of habituation and sensitization are implemented by the circuit. Emotions through visual stimuli of different intensities are induced in designed circuit. The designed circuit is able to implement emotional habituation and sensitization like humans after processing the sensory signals from vision. At the same time, based on picture stimuli as inputs, PSpice simulations confirm the feasibility and accuracy of the proposed circuit at the circuit-simulation level. The proposed circuit may serve as a potential neuromorphic module for robotic affective perception and emotional learning, providing a reference for future research on brain-inspired emotional intelligence.
Ordered waves in a memristive excitable media under asymmetrical diffusion.
Zhang Z, Guo Y, Lei Z … +1 more , Wang C
Cogn Neurodyn · 2026 Dec · PMID 42255355 · Full text
An external electric field can induce polarization effects in the excitable media and the inner diffusion of intracellular ions is modified, accompanying by appropriate shape deformation in the media. Polarization effect...
An external electric field can induce polarization effects in the excitable media and the inner diffusion of intracellular ions is modified, accompanying by appropriate shape deformation in the media. Polarization effect supports orderliness when the medium suffers from external applied electric field, and appropriate shape deformation is induced in the flexible media, including cardiac tissue and some chemical reactions. In this article, we extend an excitable medium model by incorporating both memristive electromagnetic induction and shape deformation, and further establish an equivalent network framework to investigate the resulting collective dynamics. In one-layer excitable media, the media size ( · ) maintains a constant, and shape deformation seldom changes the media size, as a result, the two diffusion coefficients ( , ) become complementary with time. The local kinetics of the memristive media is discussed, and the reaction-diffusion equations are converted into equivalent coupled neural network for exploring collective behaviors, including wave propagation and synchronization stability. The shape deformation induces changes of the diffusion coefficients in opposite way, with one direction enhanced and the other weakened, and the wave stability is modified because asymmetrical diffusion in the media. Incorporation of the memristive current and shape deformation into the excitable can better reflect the physical effect in the excitable media well.
CrossModal-associated-SNN: multi-channel spiking neural networks with clustering and associative learning.
Mo L, Liu X, Tang M
Cogn Neurodyn · 2026 Dec · PMID 42255354 · Full text
Biological sensory systems achieve remarkable robustness through efficient cross-modal integration, yet replicating this in artificial spiking neural networks (SNNs) remains a challenge. Inspired by Mayer's multi-channel...
Biological sensory systems achieve remarkable robustness through efficient cross-modal integration, yet replicating this in artificial spiking neural networks (SNNs) remains a challenge. Inspired by Mayer's multi-channel learning cognitive theory, we present CrossModal-Associated-SNN, a neuro-inspired framework that synergistically integrates visual and auditory information via Spike-Timing-Dependent Plasticity (STDP) clustering and associative learning. The architecture employs a multi-channel, multi-network design for modality-specific processing, followed by a cross-channel complementary strategy that refines decision-making through associative signals. Evaluated on small-sample benchmarks (MNIST3K and Spoken-MNIST3K), the model demonstrates superior generalization and robustness in multi-modal classification. Compared to the single-channel baseline (91% accuracy), the dual-channel dual-network improved visual and auditory recognition to 93% and 84%, respectively, with a fusion accuracy of 94%. The dual-channel triple-network architecture further maximized performance, attaining 96% (visual), 90% (auditory), and a peak 97% cross-modal fusion accuracy. These results suggest that cooperative shallow micro-networks, akin to biological small-neuron ensembles in superficial brain regions, offer a potentially energy-efficient alternative for multimodal tasks processing. CrossModal-Associated-SNN represents a critical step toward mimicking human sensory cognitive integration, offering a biologically plausible solution for energy-efficient, multi-modal intelligent systems.
Modeling time to visual insight in Mooney image recognition with a chaotic recurrent neural network.
Kimura M, Matsushita Y, Inoue M … +6 more , Seno S, Murata T, Ohzawa I, Yanagida T, Kaneko K, Hosoda K
Cogn Neurodyn · 2026 Dec · PMID 42255353 · Full text
Insight is often described as a sudden shift in, or formation of, a conceptual representation, enabling humans to restructure existing knowledge and solve problems beyond conventional analytical approaches. Although prio...
Insight is often described as a sudden shift in, or formation of, a conceptual representation, enabling humans to restructure existing knowledge and solve problems beyond conventional analytical approaches. Although prior computational studies have modeled aspects of insight using deep neural networks (DNNs) or reinforcement learning, few have captured the dynamic emergence of insight through autonomous neural computation. Here, we present a neural network model that simulates the time required to reach visual insight in the Mooney image recognition task, a widely used paradigm for studying visual insight and perceptual reorganization. The model couples a DNN module for perceptual feature extraction with a recurrent neural network (RNN) that implements a chaotic search process for recognition. The RNN is formulated as a continuous-time dynamical system, autonomously explores internal states, and stabilizes when the missing visual features required for recognition are internally reconstructed. Using the same image set as in human psychophysical experiments, the model reproduces key statistical properties of human search times (STs), including (i) lognormal-like ST distributions across participants for each image, (ii) a proportional relationship between the log-scale mean and standard deviation estimated from lognormal fits across images, and (iii) discrete levels of the fitted log-scale mean across images (a proxy for image difficulty). Importantly, these properties emerge without assuming any lognormal distribution for participant-to-participant variability, whereas previous models reproduced similar signatures by positing lognormal-distributed individual differences. We further show that lognormal-like signatures can arise from exponential search dynamics when combined with both standard experimental preprocessing and finite observation windows, highlighting the need to distinguish generative processes from measurement and analysis effects. Together, these results support a candidate generative dynamical account linking intrinsic chaotic dynamics to insight-related search and provide a computational framework for implementing insight in artificial systems.
Eligibility-trace-gated depression in predecessor feature learning enables post-reward exploration.
Seo I, Park SH, Lee H
Cogn Neurodyn · 2026 Dec · PMID 42255352 · Full text
UNLABELLED: Spatial navigation and exploration require flexible learning mechanisms that can adapt to changing environmental demands. While dopamine's role in reward prediction error is well-established, the computationa...
UNLABELLED: Spatial navigation and exploration require flexible learning mechanisms that can adapt to changing environmental demands. While dopamine's role in reward prediction error is well-established, the computational functions of other neuromodulators in reinforcement learning remain less understood. Here, we investigate how acetylcholine (ACh) modulation affects predecessor feature (PF)-based learning, a computational framework that combines successor representations with eligibility traces for retrospective credit assignment. We developed an ACh-modulated PF algorithm (ACh-PF) implementing synaptic depression via eligibility-trace outer products ([Formula: see text]), hypothesized to promote exploration by attenuating reinforcement of recently traversed transitions. Using -arm radial mazes, we compared conventional navigation (episodes terminate at reward) with a post-reward exploration criterion requiring visits to all arm endpoints. In conventional mode, all agents achieved near-optimal performance. Under the post-reward criterion, the PF baseline largely failed, whereas ACh-PF exhibited a non-monotonic dependence on [Formula: see text]: performance improved sharply within a narrow intermediate regime and degraded at higher gains, consistent with a knee-like transition followed by over-depression. The effective window narrowed with increasing spatial and action-space complexity; short two-arm mazes ([Formula: see text] - 6) supported near-ceiling reward across a broader range, whereas longer arms ([Formula: see text]) required tighter tuning and showed reduced efficiency. In multi-arm mazes, efficient exploration persisted only in the least demanding conditions (e.g., [Formula: see text]), collapsing toward timeouts as arm length and arm number increased. These results link cholinergic-like synaptic depression to flexible exploration while revealing scaling limits in complex environments. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10477-5.
fNIRS identifies right prefrontal hemodynamic signatures for subclassifying Alcohol Use Disorder.
Zhang Q, Wan L, He S … +2 more , Wang Y, Wang X
Cogn Neurodyn · 2026 Dec · PMID 42255351 · Full text
Alcohol use disorder (AUD) is a globally prevalent mental health condition, yet its classification remains largely reliant on subjective reports, lacking objective neurobiological biomarkers. This study employed function...
Alcohol use disorder (AUD) is a globally prevalent mental health condition, yet its classification remains largely reliant on subjective reports, lacking objective neurobiological biomarkers. This study employed functional near-infrared spectroscopy (fNIRS) to monitor prefrontal cortex hemodynamic activity in 80 male AUD patients, categorized into acute-onset ( = 29) and poly-symptomatic ( = 51) groups. During an alcohol cue exposure task, we measured oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentrations across 24 prefrontal channels. Significant group differences were observed in HbO levels at channels 4, 5, 7, 10, 11, 14, and 15, with channel 4 showing the most pronounced variation. Channel 13 exhibited significant differences in HbR. Linear and logistic regression analyses identified HbO in channels 4 and 5, along with HbR in channel 13, as effective predictors of AUD subtype classification under alcohol cue stimulation. These findings underscore the critical role of the right prefrontal cortex, particularly the right middle frontal gyrus, in AUD pathophysiology. Our results support the utility of fNIRS-derived hemodynamic biomarkers for objective AUD classification and suggest potential avenues for targeted intervention strategies.
Influence of higher-order interactions on collective behaviors of memristive Hindmarsh-Rose neuronal network.
Li H, Xie Y
Cogn Neurodyn · 2026 Dec · PMID 42245906 · Full text
Collective dynamics in neuronal networks arise from complex interactions among neurons and are not restricted to simple pairwise couplings. Recent studies suggest that higher-order interactions, involving groups of neuro...
Collective dynamics in neuronal networks arise from complex interactions among neurons and are not restricted to simple pairwise couplings. Recent studies suggest that higher-order interactions, involving groups of neurons acting collectively, play an important role in shaping large-scale brain dynamics. In this work, we investigate the impact of higher-order interactions on the collective behavior of memristive neuronal networks by constructing a nonlocally coupled Hindmarsh-Rose network that incorporates magnetic flux effects. By systematically varying the higher-order coupling strength and the intensity of the magnetic flux effect, we show that higher-order interactions substantially enhance synchronization and reshape the parameter regions in which chimera states emerge. More importantly, the coupling between higher-order interactions and the memristive Hindmarsh-Rose network gives rise to a breathing chimera state and a phase-wave regime. These states are identified quantitatively using the strength of incoherence, discontinuity measure, and local order parameter. In particular, the breathing chimera is characterized by evaluating the strength of incoherence over different time windows. We further show that the intensity of magnetic flux changes the firing pattern of isolated neurons and thereby alters the synchronization pathway and the threshold of coupling strength for synchronization. These results reveal that collective neuronal dynamics are governed not only by coupling strength but also by the structure of interactions, highlighting the crucial role of higher-order interactions in complex neuronal systems.
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