Front Comput Neurosci [JOURNAL]
Sun
200 papers
Neuromorphic energy economics: toward biologically inspired and sustainable power market design.
Ye A, Xu D, Li Y … +3 more , Du J, Wu Z, Tang J
Front Comput Neurosci · 2025 · PMID 40495879 · Full text
Abstract loading — click title to view on PubMed.
iSeizdiag: toward the framework development of epileptic seizure detection for healthcare.
Sharma A, Saxena A, Agrawal M … +5 more , Kishor K, Kaushik D, Jain P, Yadav AR, Saikia MJ
Front Comput Neurosci · 2025 · PMID 40495878 · Full text
INTRODUCTION: The seizure episodes result from abnormal and excessive electrical discharges by a group of brain cells. EEG framework-based signal acquisition is the real-time module that records the electrical discharges...
INTRODUCTION: The seizure episodes result from abnormal and excessive electrical discharges by a group of brain cells. EEG framework-based signal acquisition is the real-time module that records the electrical discharges produced by the brain cells. The electrical discharges are amplified and appear as a graph on electroencephalogram systems. Different neurological disorders are represented as different waves on EEG records. METHOD: This paper involves the detection of Epilepsy which appears as rapid spiking on electroencephalogram signals, using feature extraction and machine learning techniques. Various models, such as the Support Vector Machine, K Nearest Neighbor, and random forest, have been trained, and accuracy has been analyzed to predict the seizure. RESULT: An average accuracy of 95% has been claimed using the optimized model for epileptic seizure detection during training and validation. During the analysis of multiple models, the 97% accuracy is claimed after testing. Some statistical parameters are calculated to justify the optimized framework. DISCUSSION: The proposed approach represents a satisfactory contribution in precise detection for smart healthcare.
Regulation of sharp wave-ripples by cholecystokinin-expressing interneurons and parvalbumin-expressing basket cells in the hippocampal CA3 region.
Yang Y, Sun X
Front Comput Neurosci · 2025 · PMID 40492140 · Full text
To explore the individual and interactive effects of the interneurons cholecystokinin-expressing interneurons (CCKs) and parvalbumin-expressing basket cells (BCs) on sharp wave-ripples (SWR) and the underlying mechanisms...
To explore the individual and interactive effects of the interneurons cholecystokinin-expressing interneurons (CCKs) and parvalbumin-expressing basket cells (BCs) on sharp wave-ripples (SWR) and the underlying mechanisms, we constructed a mathematical model of the hippocampal CA3 network. By modulating the activity of CCKs and BCs, it was verified that CCKs inhibit the generation of SWR, while the activity of BCs affects the occurrence of SWR. Additionally, it was postulated that CCKs exert an influence on SWR through a direct mechanism, wherein CCKs directly modulate pyramidal cells (PCs). It was also discovered that BCs control SWR mainly through mutual inhibition among BCs. Furthermore, by adjusting the strength of the interaction between BCs and CCKs at various levels, it was identified that the interaction between these two types of interneurons has a relatively symmetrical effect on the regulation of SWR, functioning through a mutual inhibition mechanism. Our findings not only offer a deeper understanding of how CCKs and BCs independently regulate the generation of SWR but also provide novel insights into how changes in the strength of their interaction affect network oscillations. The results emphasize the crucial role of inhibitory interneurons in maintaining normal hippocampal oscillations, which are essential for proper brain function, particularly in the domains of memory consolidation and cognitive processes.
Reinforced liquid state machines-new training strategies for spiking neural networks based on reinforcements.
Krenzer D, Bogdan M
Front Comput Neurosci · 2025 · PMID 40488121 · Full text
INTRODUCTION: Feedback and reinforcement signals in the brain act as natures sophisticated teaching tools, guiding neural circuits to self-organization, adaptation, and the encoding of complex patterns. This study invest...
INTRODUCTION: Feedback and reinforcement signals in the brain act as natures sophisticated teaching tools, guiding neural circuits to self-organization, adaptation, and the encoding of complex patterns. This study investigates the impact of two feedback mechanisms within a deep liquid state machine architecture designed for spiking neural networks. METHODS: The Reinforced Liquid State Machine architecture integrates liquid layers, a winner-takes-all mechanism, a linear readout layer, and a novel reward-based reinforcement system to enhance learning efficacy. While traditional Liquid State Machines often employ unsupervised approaches, we introduce strict feedback to improve network performance by not only reinforcing correct predictions but also penalizing wrong ones. RESULTS: Strict feedback is compared to another strategy known as forgiving feedback, excluding punishment, using evaluations on the Spiking Heidelberg data. Experimental results demonstrate that both feedback mechanisms significantly outperform the baseline unsupervised approach, achieving superior accuracy and adaptability in response to dynamic input patterns. DISCUSSION: This comparative analysis highlights the potential of feedback integration in deepened Liquid State Machines, offering insights into optimizing spiking neural networks through reinforcement-driven architectures.
Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive.
Pastorelli E, Yegenoglu A, Kolodziej N … +5 more , Wybo W, Simula F, Diaz-Pier S, Storm JF, Paolucci PS
Front Comput Neurosci · 2025 · PMID 40463628 · Full text
Mounting experimental evidence suggests the hypothesis that brain-state-specific neural mechanisms, supported by the connectome shaped by evolution, could play a crucial role in integrating past and contextual knowledge...
Mounting experimental evidence suggests the hypothesis that brain-state-specific neural mechanisms, supported by the connectome shaped by evolution, could play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e.g., from sensory systems). These mechanisms would operate across multiple spatial and temporal scales, necessitating dedicated support at the levels of individual neurons and synapses. A notable feature within the neocortex is the structure of large, deep pyramidal neurons, which exhibit a distinctive separation between an apical dendritic compartment and a basal dendritic/perisomatic compartment. This separation is characterized by distinct patterns of incoming connections and three brain-state-specific activation mechanisms, namely, apical-amplification, -isolation, and drive, which have been proposed to be associated - with wakefulness, deeper NREM sleep stages, and REM sleep, respectively. The cognitive roles of apical mechanisms have been supported by experiments in behaving animals. In contrast, classical models of learning in spiking networks are based on single-compartment neurons, lacking the ability to describe the integration of apical and basal/somatic information. This work provides the computational community with a two-compartment spiking neuron model that supports the proposed forms of brain-state-specific activity. A machine learning evolutionary algorithm, guided by a set of fitness functions, selected parameters defining neurons that express the desired apical dendritic mechanisms. The resulting spiking model can be further approximated by a piece-wise linear transfer function (ThetaPlanes) for use in large-scale bio-inspired artificial intelligence systems.
Interpretable machine learning for precision cognitive aging.
Mahamadou AJD, Rodrigues EA, Vakorin V … +2 more , Antoine V, Moreno S
Front Comput Neurosci · 2025 · PMID 40452951 · Full text
INTRODUCTION: Machine performance has surpassed human capabilities in various tasks, yet the opacity of complex models limits their adoption in critical fields such as healthcare. Explainable AI (XAI) has emerged to addr...
INTRODUCTION: Machine performance has surpassed human capabilities in various tasks, yet the opacity of complex models limits their adoption in critical fields such as healthcare. Explainable AI (XAI) has emerged to address this by enhancing transparency and trust in AI decision-making. However, a persistent gap exists between interpretability and performance, as black-box models, such as deep neural networks, often outperform white-box models, such as regression-based approaches. To bridge this gap, the Explainable Boosting Machine (EBM), a class of generalized additive models has been introduced, combining the strengths of interpretable and high-performing models. EBM may be particularly well-suited for cognitive health research, where traditional models struggle to capture nonlinear effects in cognitive aging and account for inter- and intra-individual variability. METHODS: This cross-sectional study applies EBM to investigate the relationship between demographic, environmental, and lifestyle factors, and cognitive performance in a sample of 3,482 healthy older adults. The EBM's performance is compared against Logistic Regression, Support Vector Machines, Random Forests, Multilayer Perceptron, and Extreme Gradient Boosting, evaluating predictive accuracy and interpretability. RESULTS: The findings reveal that EBM provides valuable insights into cognitive aging, surpassing traditional models while maintaining competitive accuracy with more complex machine learning approaches. Notably, EBM highlights variations in how lifestyle activities impact cognitive performance, particularly differences between engaging in and refraining from specific activities, challenging regression-based assumptions. Moreover, our results show that the effects of lifestyle factors are heterogeneous across cognitive groups, with some individuals demonstrating significant cognitive changes while others remain resilient to these influences. DISCUSSION: These findings highlight EBM's potential in cognitive aging research, offering both interpretability and accuracy to inform personalized strategies for mitigating cognitive decline. By bridging the gap between explainability and performance, this study advances the use of XAI in healthcare and cognitive aging research.
Machine learning identifies genes linked to neurological disorders induced by equine encephalitis viruses, traumatic brain injuries, and organophosphorus nerve agents.
Yin L, VanderGiessen M, Kumar V … +7 more , Conacher B, Chao PH, Theus M, Johnson E, Kehn-Hall K, Wu X, Xie H
Front Comput Neurosci · 2025 · PMID 40433315 · Full text
Venezuelan, eastern, and western equine encephalitis viruses (collectively referred to as equine encephalitis viruses---EEV) cause serious neurological diseases and pose a significant threat to the civilian population an...
Venezuelan, eastern, and western equine encephalitis viruses (collectively referred to as equine encephalitis viruses---EEV) cause serious neurological diseases and pose a significant threat to the civilian population and the warfighter. Likewise, organophosphorus nerve agents (OPNA) are highly toxic chemicals that pose serious health threats of neurological deficits to both military and civilian personnel around the world. Consequently, only a select few approved research groups are permitted to study these dangerous chemical and biological warfare agents. This has created a significant gap in our scientific understanding of the mechanisms underlying neurological diseases. Valuable insights may be gleaned by drawing parallels to other extensively researched neuropathologies, such as traumatic brain injuries (TBI). By examining combined gene expression profiles, common and unique molecular characteristics may be discovered, providing new insights into medical countermeasures (MCMs) for TBI, EEV infection and OPNA neuropathologies and sequelae. In this study, we collected transcriptomic datasets for neurological disorders caused by TBI, EEV, and OPNA injury, and implemented a framework to normalize and integrate gene expression datasets derived from various platforms. Effective machine learning approaches were developed to identify critical genes that are either shared by or distinctive among the three neuropathologies. With the aid of deep neural networks, we were able to extract important association signals for accurate prediction of different neurological disorders by using integrated gene expression datasets of VEEV, OPNA, and TBI samples. Gene ontology and pathway analyses further identified neuropathologic features with specific gene product attributes and functions, shedding light on the fundamental biology of these neurological disorders. Collectively, we highlight a workflow to analyze published transcriptomic data using machine learning, which can be used for both identification of gene biomarkers that are unique to specific neurological conditions, as well as genes shared across multiple neuropathologies. These shared genes could serve as potential neuroprotective drug targets for conditions like EEV, TBI, and OPNA.
Computational analysis of learning in young and ageing brains.
Hewavitharana J, Steinhofel K, Giese KP … +2 more , Ierardi CM, Anand A
Front Comput Neurosci · 2025 · PMID 40395500 · Full text
Learning and memory are fundamental processes of the brain which are essential for acquiring and storing information. However, with ageing the brain undergoes significant changes leading to age-related cognitive decline....
Learning and memory are fundamental processes of the brain which are essential for acquiring and storing information. However, with ageing the brain undergoes significant changes leading to age-related cognitive decline. Although there are numerous studies on computational models and approaches which aim to mimic the learning process of the brain, they often concentrate on generic neural function exhibiting the potential need for models that address age-related changes in learning. In this paper, we present a computational analysis focusing on the differences in learning between young and old brains. Using a bipartite graph as an artificial neural network to model the synaptic connections, we simulate the learning processes of young and older brains by applying distinct biologically inspired synaptic weight update rules. Our results demonstrate the quicker learning ability of young brains compared to older ones, consistent with biological observations. Our model effectively mimics the fundamental mechanisms of the brain related to the speed of learning and reveals key insights on memory consolidation.
Intelligent rehabilitation in an aging population: empowering human-machine interaction for hand function rehabilitation through 3D deep learning and point cloud.
Xing Z, Meng Z, Zheng G … +6 more , Ma G, Yang L, Guo X, Tan L, Jiang Y, Wu H
Front Comput Neurosci · 2025 · PMID 40386804 · Full text
Human-machine interaction and computational neuroscience have brought unprecedented application prospects to the field of medical rehabilitation, especially for the elderly population, where the decline and recovery of h...
Human-machine interaction and computational neuroscience have brought unprecedented application prospects to the field of medical rehabilitation, especially for the elderly population, where the decline and recovery of hand function have become a significant concern. Responding to the special needs under the context of normalized epidemic prevention and control and the aging trend of the population, this research proposes a method based on a 3D deep learning model to process laser sensor point cloud data, aiming to achieve non-contact gesture surface feature analysis for application in the field of intelligent rehabilitation of human-machine interaction hand functions. By integrating key technologies such as the collection of hand surface point clouds, local feature extraction, and abstraction and enhancement of dimensional information, this research has constructed an accurate gesture surface feature analysis system. In terms of experimental results, this research validated the superior performance of the proposed model in recognizing hand surface point clouds, with an average accuracy of 88.72%. The research findings are of significant importance for promoting the development of non-contact intelligent rehabilitation technology for hand functions and enhancing the safe and comfortable interaction methods for the elderly and rehabilitation patients.
Synaptic plasticity facilitates oscillations in a V1 cortical column model with multiple interneuron types.
Moreni G, Zou L, Pennartz CMA … +1 more , Mejias JF
Front Comput Neurosci · 2025 · PMID 40370493 · Full text
Neural rhythms are ubiquitous in cortical recordings, but it is unclear whether they emerge due to the basic structure of cortical microcircuits or depend on function. Using detailed electrophysiological and anatomical d...
Neural rhythms are ubiquitous in cortical recordings, but it is unclear whether they emerge due to the basic structure of cortical microcircuits or depend on function. Using detailed electrophysiological and anatomical data of mouse V1, we explored this question by building a spiking network model of a cortical column incorporating pyramidal cells, PV, SST, and VIP inhibitory interneurons, and dynamics for AMPA, GABA, and NMDA receptors. The resulting model matched cell-type-specific firing rates for spontaneous and stimulus-evoked conditions in mice, although rhythmic activity was absent. Upon introduction of long-term synaptic plasticity in the form of an STDP rule, broad-band (15-60 Hz) oscillations emerged, with feedforward/feedback input streams enhancing/suppressing the oscillatory drive, respectively. These plasticity-triggered rhythms relied on all cell types, and specific experience-dependent connectivity patterns were required to generate oscillations. Our results suggest that neural rhythms are not necessarily intrinsic properties of cortical circuits, but rather they may arise from structural changes elicited by learning-related mechanisms.
Engineered biological neuronal networks as basic logic operators.
Küchler J, Vulić K, Yao H … +4 more , Valmaggia C, Ihle SJ, Weaver S, Vörös J
Front Comput Neurosci · 2025 · PMID 40357001 · Full text
We present an neuronal network with controlled topology capable of performing basic Boolean computations, such as NAND and OR. Neurons cultured within polydimethylsiloxane (PDMS) microstructures on high-density microele...
We present an neuronal network with controlled topology capable of performing basic Boolean computations, such as NAND and OR. Neurons cultured within polydimethylsiloxane (PDMS) microstructures on high-density microelectrode arrays (HD-MEAs) enable precise interaction through extracellular voltage stimulation and spiking activity recording. The architecture of our system allows for creating non-linear functions with two inputs and one output. Additionally, we analyze various encoding schemes, comparing the limitations of rate coding with the potential advantages of spike-timing-based coding strategies. This work contributes to the advancement of hybrid intelligence and biocomputing by offering insights into neural information encoding and decoding with the potential to create fully biological computational systems.
Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning.
Umair M, Ahmad J, Alasbali N … +4 more , Saidani O, Hanif M, Khattak AA, Khan MS
Front Comput Neurosci · 2025 · PMID 40313734 · Full text
INTRODUCTION: Major Depressive Disorder (MDD) remains a critical mental health concern, necessitating accurate detection. Traditional approaches to diagnosing MDD often rely on manual Electroencephalography (EEG) analysi...
INTRODUCTION: Major Depressive Disorder (MDD) remains a critical mental health concern, necessitating accurate detection. Traditional approaches to diagnosing MDD often rely on manual Electroencephalography (EEG) analysis to identify potential disorders. However, the inherent complexity of EEG signals along with the human error in interpreting these readings requires the need for more reliable, automated methods of detection. METHODS: This study utilizes EEG signals to classify MDD and healthy individuals through a combination of machine learning, deep learning, and split learning approaches. State of the art machine learning models i.e., Random Forest, Support Vector Machine, and Gradient Boosting are utilized, while deep learning models such as Transformers and Autoencoders are selected for their robust feature-extraction capabilities. Traditional methods for training machine learning and deep learning models raises data privacy concerns and require significant computational resources. To address these issues, the study applies a split learning framework. In this framework, an ensemble learning technique has been utilized that combines the best performing machine and deep learning models. RESULTS: Results demonstrate a commendable classification performance with certain ensemble methods, and a Transformer-Random Forest combination achieved 99% accuracy. In addition, to address data-sharing constraints, a split learning framework is implemented across three clients, yielding high accuracy (over 95%) while preserving privacy. The best client recorded 96.23% accuracy, underscoring the robustness of combining Transformers with Random Forest under resource-constrained conditions. DISCUSSION: These findings demonstrate that distributed deep learning pipelines can deliver precise MDD detection from EEG data without compromising data security. Proposed framework keeps data on local nodes and only exchanges intermediate representations. This approach meets institutional privacy requirements while providing robust classification outcomes.
TourismNeuro xLSTM: neuro-inspired xLSTM for rural tourism planning and innovation.
Jiang J, Li Y
Front Comput Neurosci · 2025 · PMID 40271229 · Full text
INTRODUCTION: Tourism planning, particularly in rural areas, presents complex challenges due to the highly dynamic and interdependent nature of tourism demand, influenced by seasonal, geographical, and economic factors....
INTRODUCTION: Tourism planning, particularly in rural areas, presents complex challenges due to the highly dynamic and interdependent nature of tourism demand, influenced by seasonal, geographical, and economic factors. Traditional tourism forecasting methods, such as ARIMA and Prophet, often rely on statistical models that are limited in their ability to capture long-term dependencies and multi-dimensional data interactions. These methods struggle with sparse and irregular data commonly found in rural tourism datasets, leading to less accurate predictions and suboptimal decision-making. METHODS: To address these issues, we propose NeuroTourism xLSTM, a neuro-inspired model designed to handle the unique complexities of rural tourism planning. Our model integrates an extended Long Short-Term Memory (xLSTM) framework with spatial and temporal attention mechanisms and a memory module, enabling it to capture both short-term fluctuations and long-term trends in tourism data. Additionally, the model employs a multi-objective optimization framework to balance competing goals such as revenue maximization, environmental sustainability, and socio-economic development. RESULTS: Experimental results on four diverse datasets, including ETT, M4, Weather2K, and the Tourism Forecasting Competition datasets, demonstrate that NeuroTourism xLSTM significantly outperforms traditional methods in terms of accuracy. DISCUSSION: The model's ability to process complex data dependencies and deliver precise predictions makes it a valuable tool for rural tourism planners, offering actionable insights that can enhance strategic decision-making and resource allocation.
Editorial: Hippocampal function and reinforcement learning.
Daou A, Lee H
Front Comput Neurosci · 2025 · PMID 40264710 · Full text
Abstract loading — click title to view on PubMed.
Further -Frame networking dynamics of conscious observer-self agents via a functional contextual interface: predictive coding, double-slit quantum mechanical experiment, and decision-making fallacy modeling as applied to the measurement problem in humans and AI.
Edwards DJ
Front Comput Neurosci · 2025 · PMID 40235846 · Full text
Artificial intelligence (AI) has made some remarkable advances in recent years, particularly within the area of large language models (LLMs) that produce human-like conversational abilities via utilizing transformer-base...
Artificial intelligence (AI) has made some remarkable advances in recent years, particularly within the area of large language models (LLMs) that produce human-like conversational abilities via utilizing transformer-based architecture. These advancements have sparked growing calls to develop tests not only for intelligence but also for consciousness. However, existing benchmarks assess reasoning abilities across various domains but fail to directly address consciousness. To bridge this gap, this paper introduces the functional contextual -Frame model, a novel framework integrating predictive coding, quantum Bayesian (QBism), and evolutionary dynamics. This comprehensive model explicates how conscious observers, whether human or artificial, should update beliefs and interact within a quantum cognitive system. It provides a dynamic account of belief evolution through the interplay of internal observer states and external stimuli. By modeling decision-making fallacies such as the conjunction fallacy and conscious intent collapse experiments within this quantum probabilistic framework, the -Frame model establishes structural and functional equivalence between cognitive processes identified within these experiments and traditional quantum mechanics (QM). It is hypothesized that consciousness serves as an active participant in wavefunction collapse (or actualization of the physical definite states we see), bridging quantum potentiality and classical outcomes via internal observer states and contextual interactions via a self-referential loop. This framework formalizes decision-making processes within a Hilbert space, mapping cognitive states to quantum operators and contextual dependencies, and demonstrates structural and functional equivalence between cognitive and quantum systems in order to address the measurement problem. Furthermore, the model extends to testable predictions about AI consciousness by specifying informational boundaries, contextual parameters, and a conscious-time dimension derived from Anti-de Sitter/Conformal Field Theory correspondence (AdS/CFT). This paper theorizes that human cognitive biases reflect adaptive, evolutionarily stable strategies that optimize predictive accuracy (i.e., evolved quantum heuristic strategies rather than errors relative to classical rationality) under uncertainty within a quantum framework, challenging the classical interpretation of irrationality. The -Frame model offers a unified account of consciousness, decision-making, behavior, and quantum mechanics, incorporating the idea of finding truth without proof (thus overcoming Gödelian uncertainty), insights from quantum probability theory (such as the Linda cognitive bias findings), and the possibility that consciousness can cause waveform collapse (or perturbation) accounting for the measurement problem. It proposes a process for conscious time and branching worldlines to explain subjective experiences of time flow and conscious free will. These theoretical advancements provide a foundation for interdisciplinary exploration into consciousness, cognition, and quantum systems, offering a path toward developing tests for AI consciousness and addressing the limitations of classical computation in representing conscious agency.
Prefrontal meta-control incorporating mental simulation enhances the adaptivity of reinforcement learning agents in dynamic environments.
Kim J, Lee JH
Front Comput Neurosci · 2025 · PMID 40212888 · Full text
INTRODUCTION: Recent advances in computational neuroscience highlight the significance of prefrontal cortical meta-control mechanisms in facilitating flexible and adaptive human behavior. In addition, hippocampal functio...
INTRODUCTION: Recent advances in computational neuroscience highlight the significance of prefrontal cortical meta-control mechanisms in facilitating flexible and adaptive human behavior. In addition, hippocampal function, particularly mental simulation capacity, proves essential in this adaptive process. Rooted from these neuroscientific insights, we present , a novel neuroscience-inspired reinforcement learning architecture that demonstrates rapid adaptation to environmental dynamics whilst managing variable goal states and state-transition uncertainties. METHODS: This architectural framework implements prefrontal meta-control mechanisms integrated with hippocampal replay function, which in turn optimized task performance with limited experiences. We evaluated this approach through comprehensive experimental simulations across three distinct paradigms: the two-stage Markov decision task, which frequently serves in human learning and decision-making research; , an established benchmark suite for model-based reinforcement learning; and a variant incorporating multiple goals under uncertainty. RESULTS: Experimental results demonstrate 's superior performance compared with baseline reinforcement learning algorithms across multiple metrics: average reward, choice optimality, and a number of trials for success. DISCUSSIONS: These findings advance our understanding of computational reinforcement learning whilst contributing to the development of brain-inspired learning agents capable of flexible, goal-directed behavior within dynamic environments.
NeuroFusionNet: cross-modal modeling from brain activity to visual understanding.
Lang K, Fang J, Su G
Front Comput Neurosci · 2025 · PMID 40207297 · Full text
In recent years, the integration of machine vision and neuroscience has provided a new perspective for deeply understanding visual information. This paper proposes an innovative deep learning model, NeuroFusionNet, desig...
In recent years, the integration of machine vision and neuroscience has provided a new perspective for deeply understanding visual information. This paper proposes an innovative deep learning model, NeuroFusionNet, designed to enhance the understanding of visual information by integrating fMRI signals with image features. Specifically, images are processed by a visual model to extract region-of-interest (ROI) features and contextual information, which are then encoded through fully connected layers. The fMRI signals are passed through 1D convolutional layers to extract features, effectively preserving spatial information and improving computational efficiency. Subsequently, the fMRI features are embedded into a 3D voxel representation to capture the brain's activity patterns in both spatial and temporal dimensions. To accurately model the brain's response to visual stimuli, this paper introduces a Mutli-scale fMRI Timeformer module, which processes fMRI signals at different scales to extract both fine details and global responses. To further optimize the model's performance, we introduce a novel loss function called the fMRI-guided loss. Experimental results show that NeuroFusionNet effectively integrates image and brain activity information, providing more precise and richer visual representations for machine vision systems, with broad potential applications.
Exploring the neural basis of creativity: EEG analysis of power spectrum and functional connectivity during creative tasks in school-aged children.
Krumm G, Arán Filippetti V, Catanzariti M … +1 more , Mateos DM
Front Comput Neurosci · 2025 · PMID 40145081 · Full text
Creativity is a fundamental aspect of human cognition, particularly during childhood. Exploring creativity through electroencephalography (EEG) provides valuable insights into the brain mechanisms underlying this vital c...
Creativity is a fundamental aspect of human cognition, particularly during childhood. Exploring creativity through electroencephalography (EEG) provides valuable insights into the brain mechanisms underlying this vital cognitive process. This study analyzed the power spectrum and functional connectivity of interhemispheric and intrahemispheric brain activity during creative tasks in 15 Argentine children aged 9 to 12, using a 14-channel EEG system. The Torrance test of creative thinking (TTCT) was used, incorporating one figural and one verbal task. EEG metrics included relative power spectral density (rPSD) across Delta, Theta, Alpha, Beta, and Gamma bands. Spearman's Rho correlations were calculated between frequency bands and performance on creativity tasks, followed by functional connectivity assessment through coherence analysis across the [1-50] Hz spectrum. The results revealed significant increases in rPSD across all frequency bands during creative tasks compared to rest, with no significant differences between figural and verbal tasks. Correlational analysis revealed positive associations between the Beta band and the innovative and adaptive factors of the figural task. In contrast, for the verbal task, both the Beta and Gamma bands were positively related to flexibility, while the Alpha band showed a negative relationship with fluency and originality. Coherence analysis showed enhanced intrahemispheric synchronization, particularly in frontotemporal and temporo-occipital regions, alongside reduced interhemispheric frontal coherence. These findings suggest that creativity in children involves a dynamic reorganization of brain activity, characterized by oscillatory activation and region-specific connectivity changes. Our study contributes to a deeper understanding of the brain mechanisms supporting creativity during child development.
AD-Diff: enhancing Alzheimer's disease prediction accuracy through multimodal fusion.
Han L
Front Comput Neurosci · 2025 · PMID 40145080 · Full text
Early prediction of Alzheimer's disease (AD) is crucial to improving patient quality of life and treatment outcomes. However, current predictive methods face challenges such as insufficient multimodal information integra...
Early prediction of Alzheimer's disease (AD) is crucial to improving patient quality of life and treatment outcomes. However, current predictive methods face challenges such as insufficient multimodal information integration and the high cost of PET image acquisition, which limit their effectiveness in practical applications. To address these issues, this paper proposes an innovative model, AD-Diff. This model significantly improves AD prediction accuracy by integrating PET images generated through a diffusion process with cognitive scale data and other modalities. Specifically, the AD-Diff model consists of two core components: the ADdiffusion module and the multimodal Mamba Classifier. The ADdiffusion module uses a 3D diffusion process to generate high-quality PET images, which are then fused with MRI images and tabular data to provide input for the Multimodal Mamba Classifier. Experimental results on the OASIS and ADNI datasets demonstrate that the AD-Diff model performs exceptionally well in both long-term and short-term AD prediction tasks, significantly improving prediction accuracy and reliability. These results highlight the significant advantages of the AD-Diff model in handling complex medical image data and multimodal information, providing an effective tool for the early diagnosis and personalized treatment of Alzheimer's disease.
Editorial: Computational intelligence for signal and image processing, volume II.
Koundal D, Tohka J
Front Comput Neurosci · 2025 · PMID 40135147 · Full text
Abstract loading — click title to view on PubMed.
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