کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5736966 1614503 2017 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Using computational theory to constrain statistical models of neural data
ترجمه فارسی عنوان
با استفاده از تئوری محاسباتی برای محدود کردن مدل های آماری داده های عصبی
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب (عمومی)
چکیده انگلیسی
Computational neuroscience is, to first order, dominated by two approaches: the 'bottom-up' approach, which searches for statistical patterns in large-scale neural recordings, and the 'top-down' approach, which begins with a theory of computation and considers plausible neural implementations. While this division is not clear-cut, we argue that these approaches should be much more intimately linked. From a Bayesian perspective, computational theories provide constrained prior distributions on neural data - albeit highly sophisticated ones. By connecting theory to observation via a probabilistic model, we provide the link necessary to test, evaluate, and revise our theories in a data-driven and statistically rigorous fashion. This review highlights examples of this theory-driven pipeline for neural data analysis in recent literature and illustrates it with a worked example based on the temporal difference learning model of dopamine.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Current Opinion in Neurobiology - Volume 46, October 2017, Pages 14-24
نویسندگان
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