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Abstract
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| 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. | |
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International
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Si |
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Congress
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23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010 |
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960 |
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Place
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Córdoba, España |
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Reviewers
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Si |
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ISBN/ISSN
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3-642-13032-1 |
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10.1007/978-3-642-13033-5_16 |
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Start Date
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01/06/2010 |
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End Date
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04/06/2010 |
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From page
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149 |
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To page
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158 |
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Trends in Applied Intelligent Systems |