Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Towards a network theory of cognition
Neural Networks - Special issue on the global brain: imaging and modelling
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Polychronization: Computation with Spikes
Neural Computation
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Mining probabilistic models learned by EDAs in the optimization of multi-objective problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Structural inference of hierarchies in networks
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
| Hi-index | 0.00 |
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.