Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
This work deals with the design of analog circuits for Artificial Neural Networks (ANNs) controllers using an Evolvable Hardware (EHW) platform. ANNs are massively parallel system...
Background: The abundant data available for protein interaction networks have not yet been fully understood. New types of analyses are needed to reveal organizational principles o...
Taner Z. Sen, Andrzej Kloczkowski, Robert L. Jerni...
— A hormone-inspired task scheduling method is described which assigns tasks to a group of robots, taking into account the robots’ performances. This method draws on previous w...
Motivation: The functioning of biological networks depends in large part on their complex underlying structure. When studying their systemic nature many modeling approaches focus ...