Figure 2. Representation of the Elman network used in this study for the Mullus barbatus (MUT) model.

The input layer comprises: four neurons related to cohort abundances (two cohorts for males and two cohorts for females) at each time for each cell; other four neurons related to abundance of the same cohorts in the neighboring cells (ray = 2); a neuron for the fishing effort; three neurons for the sea bottom characteristics and mean annual sea surface temperature. These neurons directly propagate the information to the basic hidden neurons. At each step of the training procedure, the updated pattern of the basic hidden neurons is memorized by the context neurons and, at the successive step, propagated to the basic hidden neurons together with the new information in the input neurons. The output layer contains as many neurons as the number of cohorts in the input layer.