Synergies between network-based representations and probabilistic graphical modeling in the solution of problems from neuroscience

Synergies between network-based representations and probabilistic graphical modeling in the solution of problems from neuroscience” by Roberto Santana, Concha Bielza, and Pedro Larrañaga. In Proceedings of the Twenty Third International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, (N. Garca-Pedrajas et. al., ed.), (Córdoba, España), 2010, pp. 149-158.

Abstract

Neural systems network-based representations are useful tools to analyze numerous phenomena in neuroscience. Probabilistic graphical models (PGMs) give a concise and still rich representation of complex systems from different domains, including neural systems. In this paper we analyze the characteristics of a bidirectional relationship between networks-based representations and PGMs. We show the way in which this relationship can be exploited introducing a number of methods for the solution of classification, inference and optimization problems. To illustrate the applicability of the introduced methods, a number of problems from the field of neuroscience, in which ongoing research is conducted, are used.

BibTeX entry:

@inproceedings{Santana_et_al:2010a,
   author = {Roberto Santana and Concha Bielza and Pedro Larra{\~n}aga},
   editor = {N. Garca-Pedrajas et. al.,},
   title = {Synergies between network-based representations and
	probabilistic graphical modeling in the solution of problems from
	neuroscience},
   booktitle = {Proceedings of the Twenty Third International Conference
	on Industrial, Engineering and Other Applications of Applied
	Intelligent Systems},
   series = {Lecture Notes in Artificial Intelligence},
   volume = {6098},
   pages = {149-158},
   publisher = {Springer},
   address = {C{\'o}rdoba, Espa{\~n}a},
   year = {2010},
   url = {http://dx.doi.org/10.1007/978-3-642-13033-5_16}
}

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