Inferring and quantifying causality in neuronal networks
نویسندگان
چکیده
منابع مشابه
A mathematical framework for inferring connectivity in probabilistic neuronal networks.
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 i...
متن کاملBank’s Corporate Governance: Quantifying the Effects in Iranian Banking Networks
The most important tool for promoting the bank’s stability and health is the establishment of a standard corporate governance structure for managing the bank's business. Redesigning the relationships between bank management, shareholders and the rest of the bank’s stockholder, including the objectives, the risk and audit indices, and internal control of the bank, is recognized as the foundation...
متن کاملCausality and Networks
Causality is omnipresent in scientists’ verbalisations of their understanding, even though we have no formal consensual scientific definition for it. In Automata Networks, it suffices to say that automata “influence” one another to introduce a notion of causality. One might argue that this merely is an incidental side effect of preferring statements expressed in natural languages to mathematica...
متن کاملCausality in Political Networks
Investigations of American politics have increasingly turned to analyses of political networks to understand public opinion, voting behavior, the diffusion of policy ideas, bill sponsorship in the legislature, interest group coalitions and influence, party factions, institutional development, and other empirical phenomena. While the association between political networks and political behavior ...
متن کاملCausality Networks
While correlation measures are used to discern statistical relationships between observed variables in almost all branches of datadriven scientific inquiry, what we are really interested in is the existence of causal dependence. Statistical tests for causality, it turns out, are significantly harder to construct; the difficulty stemming from both philosophical hurdles in making precise the noti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Neuroscience
سال: 2011
ISSN: 1471-2202
DOI: 10.1186/1471-2202-12-s1-p192