383 recherche sur le mot-clé 'neural-network-models'

How neuropsychology informs our understanding of developmental disorders / Bruce F. PENNINGTON in Journal of Child Psychology and Psychiatry, 50-1-2 (January/February 2009)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 50-1-2 (January/February 2009) . - p.72-78 Titre : How neuropsychology informs our understanding of developmental disorders Type de document : texte imprimé Auteurs : Bruce F. PENNINGTON, Auteur Année de publication : 2009 Article en page(s) : p.72-78 Langues : Anglais (eng) Mots-clés : Developmental-cognitive-neuroscience plasticity molecular-genetics neural-network-models dyslexia neuropsychology Index. décimale : PER Périodiques Résumé : This review includes 1) an explanation of what neuropsychology is, 2) a brief history of how developmental cognitive neuroscience emerged from earlier neuropsychological approaches to understanding atypical development, 3) three recent examples that illustrate the benefits of this approach, 4) issues and challenges this approach must face, and 5) a forecast for the future of this approach. En ligne : http://dx.doi.org/10.1111/j.1469-7610.2008.01977.x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=694 [article] How neuropsychology informs our understanding of developmental disorders [texte imprimé] / Bruce F. PENNINGTON, Auteur . - 2009 . - p.72-78. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 50-1-2 (January/February 2009) . - p.72-78 Mots-clés : Developmental-cognitive-neuroscience plasticity molecular-genetics neural-network-models dyslexia neuropsychology Index. décimale : PER Périodiques Résumé : This review includes 1) an explanation of what neuropsychology is, 2) a brief history of how developmental cognitive neuroscience emerged from earlier neuropsychological approaches to understanding atypical development, 3) three recent examples that illustrate the benefits of this approach, 4) issues and challenges this approach must face, and 5) a forecast for the future of this approach. En ligne : http://dx.doi.org/10.1111/j.1469-7610.2008.01977.x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=694

Artificial Intelligence Networks Combining Histopathology and Machine Learning Can Extract Axon Pathology in Autism Spectrum Disorder / Arash YAZDANBAKHSH in Autism Research, 18-11 (November 2025)  

Public ISBD [article]  inAutism Research > 18-11 (November 2025) . - p.2210-2230 Titre : Artificial Intelligence Networks Combining Histopathology and Machine Learning Can Extract Axon Pathology in Autism Spectrum Disorder Type de document : texte imprimé Auteurs : Arash YAZDANBAKHSH, Auteur ; Kim T.M. DANG, Auteur ; Kelvin KUANG, Auteur ; Tingru LIAN, Auteur ; Xuefeng LIU, Auteur ; Songlin XIE, Auteur ; Basilis ZIKOPOULOS, Auteur Article en page(s) : p.2210-2230 Langues : Anglais (eng) Mots-clés : anterior cingulate cortex  convolutional neural network  deep neural network  long-range pathways  short-range pathways  white matter Index. décimale : PER Périodiques Résumé : ABSTRACT Axon features that underlie the structural and functional organization of cortical pathways have distinct patterns in the brains of neurotypical controls (CTR) compared to individuals with Autism Spectrum Disorder (ASD). However, detailed axon study demands labor-intensive surveys and time-consuming analysis of microscopic sections from postmortem human brain tissue, making it challenging to systematically examine large regions of the brain. To address these challenges, we developed an approach that uses machine learning to automatically classify microscopic sections from ASD and CTR brains, while also considering different white matter regions: superficial white matter (SWM), which contains a majority of axons that connect nearby cortical areas, and deep white matter (DWM), which is comprised exclusively of axons that participate in long-range pathways. The result was a deep neural network that can successfully classify the white matter below the anterior cingulate cortex (ACC) of ASD and CTR groups with 98% accuracy, while also distinguishing between DWM and SWM pathway composition with high average accuracy, up to 80%. Examination of image regions important for network classification and misclassification, through sensitivity maps, along with multidimensional scaling analysis, helped identify key pathological markers of ASD and highlighted the spectrum of ASD heterogeneity and overlaps with neurotypical characteristics. Large datasets that can be used to expand training, validation, and testing of this network have the potential to automate high-resolution microscopic analysis of postmortem brain tissue, so that it can be used to systematically study white matter across brain regions in health and disease. En ligne : https://doi.org/10.1002/aur.70135 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=571 [article] Artificial Intelligence Networks Combining Histopathology and Machine Learning Can Extract Axon Pathology in Autism Spectrum Disorder [texte imprimé] / Arash YAZDANBAKHSH, Auteur ; Kim T.M. DANG, Auteur ; Kelvin KUANG, Auteur ; Tingru LIAN, Auteur ; Xuefeng LIU, Auteur ; Songlin XIE, Auteur ; Basilis ZIKOPOULOS, Auteur . - p.2210-2230. Langues : Anglais (eng) in Autism Research > 18-11 (November 2025) . - p.2210-2230 Mots-clés : anterior cingulate cortex  convolutional neural network  deep neural network  long-range pathways  short-range pathways  white matter Index. décimale : PER Périodiques Résumé : ABSTRACT Axon features that underlie the structural and functional organization of cortical pathways have distinct patterns in the brains of neurotypical controls (CTR) compared to individuals with Autism Spectrum Disorder (ASD). However, detailed axon study demands labor-intensive surveys and time-consuming analysis of microscopic sections from postmortem human brain tissue, making it challenging to systematically examine large regions of the brain. To address these challenges, we developed an approach that uses machine learning to automatically classify microscopic sections from ASD and CTR brains, while also considering different white matter regions: superficial white matter (SWM), which contains a majority of axons that connect nearby cortical areas, and deep white matter (DWM), which is comprised exclusively of axons that participate in long-range pathways. The result was a deep neural network that can successfully classify the white matter below the anterior cingulate cortex (ACC) of ASD and CTR groups with 98% accuracy, while also distinguishing between DWM and SWM pathway composition with high average accuracy, up to 80%. Examination of image regions important for network classification and misclassification, through sensitivity maps, along with multidimensional scaling analysis, helped identify key pathological markers of ASD and highlighted the spectrum of ASD heterogeneity and overlaps with neurotypical characteristics. Large datasets that can be used to expand training, validation, and testing of this network have the potential to automate high-resolution microscopic analysis of postmortem brain tissue, so that it can be used to systematically study white matter across brain regions in health and disease. En ligne : https://doi.org/10.1002/aur.70135 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=571

Behaviorally Based Modeling and Computational Approaches to Neuroscience / George N. Jr REEKE in Annual Review of Neuroscience, 16 (1993)

Public ISBD [article]  inAnnual Review of Neuroscience > 16 (1993) . - p.597-623 Titre : Behaviorally Based Modeling and Computational Approaches to Neuroscience Type de document : texte imprimé Auteurs : George N. Jr REEKE, Auteur ; Olaf SPORNS, Auteur Année de publication : 1993 Article en page(s) : p.597-623 Langues : Anglais (eng) Mots-clés : Artificial neural network - Synthetic neural modeling - Neuronal group selection - Emergent behavior - Reinforcement learning Index. décimale : PER Périodiques Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=374 [article] Behaviorally Based Modeling and Computational Approaches to Neuroscience [texte imprimé] / George N. Jr REEKE, Auteur ; Olaf SPORNS, Auteur . - 1993 . - p.597-623. Langues : Anglais (eng) in Annual Review of Neuroscience > 16 (1993) . - p.597-623 Mots-clés : Artificial neural network - Synthetic neural modeling - Neuronal group selection - Emergent behavior - Reinforcement learning Index. décimale : PER Périodiques Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=374

Computational Models of the Neural Bases of Learning and Memory / Mark A. GLUCK in Annual Review of Neuroscience, 16 (1993)

Public ISBD [article]  inAnnual Review of Neuroscience > 16 (1993) . - p.667-706 Titre : Computational Models of the Neural Bases of Learning and Memory Type de document : texte imprimé Auteurs : Mark A. GLUCK, Auteur ; Richard GRANGER, Auteur Année de publication : 1993 Article en page(s) : p.667-706 Langues : Anglais (eng) Mots-clés : Olfactory cortex - Neural network - Connectionism - Hippocampus - Cerebellum Index. décimale : PER Périodiques Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=374 [article] Computational Models of the Neural Bases of Learning and Memory [texte imprimé] / Mark A. GLUCK, Auteur ; Richard GRANGER, Auteur . - 1993 . - p.667-706. Langues : Anglais (eng) in Annual Review of Neuroscience > 16 (1993) . - p.667-706 Mots-clés : Olfactory cortex - Neural network - Connectionism - Hippocampus - Cerebellum Index. décimale : PER Périodiques Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=374

Deep Neural Network Reveals the World of Autism From a First-Person Perspective / Mindi RUAN in Autism Research, 14-2 (February 2021)  

Public ISBD [article]  inAutism Research > 14-2 (February 2021) . - p.333-342 Titre : Deep Neural Network Reveals the World of Autism From a First-Person Perspective Type de document : texte imprimé Auteurs : Mindi RUAN, Auteur ; Paula J. WEBSTER, Auteur ; Xin LI, Auteur ; Shuo WANG, Auteur Article en page(s) : p.333-342 Langues : Anglais (eng) Mots-clés : artificial intelligence  attention  autism spectrum disorder  deep neural network  faces  photos  saliency Index. décimale : PER Périodiques Résumé : People with autism spectrum disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories, but was particularly successful at classifying photos of people with >80% accuracy. Importantly, visualization of our model revealed critical features that led to successful discrimination and showed that our model adopted a strategy similar to that of ASD experts. Furthermore, for the first time we showed that photos taken by individuals with ASD contained less salient objects, especially in the central visual field. Notably, our model outperformed classification of these photos by ASD experts. Together, we demonstrate an effective and novel method that is capable of discerning photos taken by individuals with ASD and revealing aberrant visual attention in ASD from a unique first-person perspective. Our method may in turn provide an objective measure for evaluations of individuals with ASD. LAY SUMMARY: People with autism spectrum disorder (ASD) demonstrate atypical visual attention to social stimuli. However, it remains largely unclear how they perceive the world from a first-person perspective. In this study, we employed a deep learning approach to analyze a unique dataset of photos taken by people with and without ASD. Our computer modeling was not only able to discern which photos were taken by individuals with ASD, outperforming ASD experts, but importantly, it revealed critical features that led to successful discrimination, revealing aspects of atypical visual attention in ASD from their first-person perspective. En ligne : http://dx.doi.org/10.1002/aur.2376 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=441 [article] Deep Neural Network Reveals the World of Autism From a First-Person Perspective [texte imprimé] / Mindi RUAN, Auteur ; Paula J. WEBSTER, Auteur ; Xin LI, Auteur ; Shuo WANG, Auteur . - p.333-342. Langues : Anglais (eng) in Autism Research > 14-2 (February 2021) . - p.333-342 Mots-clés : artificial intelligence  attention  autism spectrum disorder  deep neural network  faces  photos  saliency Index. décimale : PER Périodiques Résumé : People with autism spectrum disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories, but was particularly successful at classifying photos of people with >80% accuracy. Importantly, visualization of our model revealed critical features that led to successful discrimination and showed that our model adopted a strategy similar to that of ASD experts. Furthermore, for the first time we showed that photos taken by individuals with ASD contained less salient objects, especially in the central visual field. Notably, our model outperformed classification of these photos by ASD experts. Together, we demonstrate an effective and novel method that is capable of discerning photos taken by individuals with ASD and revealing aberrant visual attention in ASD from a unique first-person perspective. Our method may in turn provide an objective measure for evaluations of individuals with ASD. LAY SUMMARY: People with autism spectrum disorder (ASD) demonstrate atypical visual attention to social stimuli. However, it remains largely unclear how they perceive the world from a first-person perspective. In this study, we employed a deep learning approach to analyze a unique dataset of photos taken by people with and without ASD. Our computer modeling was not only able to discern which photos were taken by individuals with ASD, outperforming ASD experts, but importantly, it revealed critical features that led to successful discrimination, revealing aspects of atypical visual attention in ASD from their first-person perspective. En ligne : http://dx.doi.org/10.1002/aur.2376 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=441

Distinct, dosage-sensitive requirements for the autism-associated factor CHD8 during cortical development / Shaun HURLEY in Molecular Autism, 12 (2021)  

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Hippocampal neurons isolated from rats subjected to the valproic acid model mimic in vivo synaptic pattern: evidence of neuronal priming during early development in autism spectrum disorders / Marianela Evelyn TRAETTA in Molecular Autism, 12 (2021)  

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Intrinsic neural circuitry of depression in adolescent females / Jingwen JIN in Journal of Child Psychology and Psychiatry, 61-4 (April 2020)  

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Modeling of Neural Circuits: What Have We Learned? / A.I. SELVERSTON in Annual Review of Neuroscience, 16 (1993)

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Modulation of Neural Networks for Behavior / Ronald M. HARRIS-WARWICK in Annual Review of Neuroscience, 14 (1991)

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