کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4501130 1320046 2007 48 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A mathematical framework for inferring connectivity in probabilistic neuronal networks
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک علوم کشاورزی و بیولوژیک (عمومی)
پیش نمایش صفحه اول مقاله
A mathematical framework for inferring connectivity in probabilistic neuronal networks
چکیده انگلیسی

We describe an approach for determining causal connections among nodes of a probabilistic network even when many nodes remain unobservable. The unobservable nodes introduce ambiguity into the estimate of the causal structure. However, in some experimental contexts, such as those commonly used in neuroscience, this ambiguity is present even without unobservable nodes. The analysis is presented in terms of a point process model of a neuronal network, though the approach can be generalized to other contexts. The analysis depends on the existence of a model that captures the relationship between nodal activity and a set of measurable external variables. The mathematical framework is sufficiently general to allow a large class of such models. The results are modestly robust to deviations from model assumptions, though additional validation methods are needed to assess the success of the results.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mathematical Biosciences - Volume 205, Issue 2, February 2007, Pages 204–251
نویسندگان
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