Frontiers in Computational Neuroscience最新文献

{"title":"iSeizdiag: toward the framework development of epileptic seizure detection for healthcare.","authors":"Ashish Sharma, Akshat Saxena, Mradul Agrawal, Kunal Kishor, Deepti Kaushik, Prateek Jain, Arvind R Yadav, Manob Jyoti Saikia","doi":"10.3389/fncom.2025.1545425","DOIUrl":"10.3389/fncom.2025.1545425","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Method: </strong>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.</p><p><strong>Result: </strong>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.</p><p><strong>Discussion: </strong>The proposed approach represents a satisfactory contribution in precise detection for smart healthcare.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1545425"},"PeriodicalIF":2.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12149152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuromorphic energy economics: toward biologically inspired and sustainable power market design.","authors":"Aoshuang Ye, Dong Xu, Yichao Li, Jiawei Du, Zhiwei Wu, Junjie Tang","doi":"10.3389/fncom.2025.1597038","DOIUrl":"10.3389/fncom.2025.1597038","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1597038"},"PeriodicalIF":2.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12149131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regulation of sharp wave-ripples by cholecystokinin-expressing interneurons and parvalbumin-expressing basket cells in the hippocampal CA3 region.","authors":"Yuchen Yang, Xiaojuan Sun","doi":"10.3389/fncom.2025.1591003","DOIUrl":"10.3389/fncom.2025.1591003","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1591003"},"PeriodicalIF":2.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12146282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144257730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforced liquid state machines-new training strategies for spiking neural networks based on reinforcements.","authors":"Dominik Krenzer, Martin Bogdan","doi":"10.3389/fncom.2025.1569374","DOIUrl":"10.3389/fncom.2025.1569374","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1569374"},"PeriodicalIF":2.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144247258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive.","authors":"Elena Pastorelli, Alper Yegenoglu, Nicole Kolodziej, Willem Wybo, Francesco Simula, Sandra Diaz-Pier, Johan Frederik Storm, Pier Stanislao Paolucci","doi":"10.3389/fncom.2025.1566196","DOIUrl":"10.3389/fncom.2025.1566196","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1566196"},"PeriodicalIF":2.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144215325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable machine learning for precision cognitive aging.","authors":"Abdoul Jalil Djiberou Mahamadou, Emma A Rodrigues, Vasily Vakorin, Violaine Antoine, Sylvain Moreno","doi":"10.3389/fncom.2025.1560064","DOIUrl":"10.3389/fncom.2025.1560064","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1560064"},"PeriodicalIF":2.1,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12122752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning identifies genes linked to neurological disorders induced by equine encephalitis viruses, traumatic brain injuries, and organophosphorus nerve agents.","authors":"Liduo Yin, Morgen VanderGiessen, Vinoth Kumar, Benjamin Conacher, Po-Chien Haku Chao, Michelle Theus, Erik Johnson, Kylene Kehn-Hall, Xiaowei Wu, Hehuang Xie","doi":"10.3389/fncom.2025.1529902","DOIUrl":"10.3389/fncom.2025.1529902","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1529902"},"PeriodicalIF":2.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12106541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144157435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational analysis of learning in young and ageing brains.","authors":"Jayani Hewavitharana, Kathleen Steinhofel, Karl Peter Giese, Carolina Moretti Ierardi, Amida Anand","doi":"10.3389/fncom.2025.1565660","DOIUrl":"10.3389/fncom.2025.1565660","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1565660"},"PeriodicalIF":2.1,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089124/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144110421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent rehabilitation in an aging population: empowering human-machine interaction for hand function rehabilitation through 3D deep learning and point cloud.","authors":"Zhizhong Xing, Zhijun Meng, Gengfeng Zheng, Guolan Ma, Lin Yang, Xiaojun Guo, Li Tan, Yuanqiu Jiang, Huidong Wu","doi":"10.3389/fncom.2025.1543643","DOIUrl":"10.3389/fncom.2025.1543643","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1543643"},"PeriodicalIF":2.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12081371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synaptic plasticity facilitates oscillations in a V1 cortical column model with multiple interneuron types.","authors":"Giulia Moreni, Licheng Zou, Cyriel M A Pennartz, Jorge F Mejias","doi":"10.3389/fncom.2025.1568143","DOIUrl":"https://doi.org/10.3389/fncom.2025.1568143","url":null,"abstract":"<p><p>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 <i>in vivo</i> 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.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1568143"},"PeriodicalIF":2.1,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144076907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}