|
Descripción
|
|
|---|---|
| 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. | |
|
Internacional
|
Si |
|
Nombre congreso
|
23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010 |
|
Tipo de participación
|
960 |
|
Lugar del congreso
|
Córdoba, España |
|
Revisores
|
Si |
|
ISBN o ISSN
|
3-642-13032-1 |
|
DOI
|
10.1007/978-3-642-13033-5_16 |
|
Fecha inicio congreso
|
01/06/2010 |
|
Fecha fin congreso
|
04/06/2010 |
|
Desde la página
|
149 |
|
Hasta la página
|
158 |
|
Título de las actas
|
Trends in Applied Intelligent Systems |