دانلود مقاله ISI انگلیسی شماره 29289
عنوان فارسی مقاله

مدل سازی شبکه های بیزی از اجماع بین کارشناسان:کاربرد برای طبقه بندی نورون

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
29289 2014 20 صفحه PDF سفارش دهید 12090 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Bayesian network modeling of the consensus between experts: An application to neuron classification
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Approximate Reasoning, Volume 55, Issue 1, Part 1, January 2014, Pages 3–22

کلمات کلیدی
شبکه های بیزی - اتفاق نظر کارشناسی - طبقه بندی نورون -
پیش نمایش مقاله
پیش نمایش مقاله مدل سازی شبکه های بیزی از اجماع بین کارشناسان:کاربرد برای طبقه بندی نورون

چکیده انگلیسی

Neuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their three-dimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology.

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