What is Hybrid Training

More than 50 years ago connectionist systems (CSs) were created with the purpose to process information in the computers like the human brain (McCulloch & Pitts, 1943). Since that time these systems have advanced considerably and nowadays they allow us to resolve complex problems in many disciplines (classification, clustering, regression, etc.). But this advance is not enough. There are still a lot of limitations when these systems are used (Dorado, 1999). Mostly the improvements were obtained following two different ways. Many researchers have preferred the construction of artificial neural networks (ANNs) based in mathematic models with diverse equations which lead its functioning (Cortes & Vapnik, 1995; Haykin, 1999). Otherwise other researchers have pretended the most possibly to make alike these systems to human brain (Rabuñal, 1999; Porto, 2004). The systems included in this article have emerged following the second way of investigation. CSs which pretend to imitate the neuroglial nets of the brain are introduced. These systems are named Artificial NeuroGlial Networks (ANGNs) (Porto, 2004). These CSs are not only made of neuron, but also from elements which imitate glial neurons named astrocytes (Araque, 1999). These systems, which have hybrid training, have demonstrated efficacy when resolving classification problems with totally connected feed-forward multilayer networks, without backpropagation and lateral connections.