This report presents a joint study of biological and computational vision. First we briefly review the most common models of neurons and neural networks and the function of cells in the V1/V2 areas of the visual cortex. Subsequently, we present the biologically plausible models for image segmentation that have been proposed by Stephen Grossberg and his collaborators during the previous two decades in a series of papers. We have implemented the B.C.S. (Boundary Contour System) and F.C.S. (Feature Contour System) models that form the basic building blocks of this model of biological vision, known as FACADE (Form And Colour and DEpth) theory. During their implementation, we faced several problems, like a large number of parameters and instability with respect to these; this was not traded off with a higher performance when compared to classical computer vision algorithms. This has led us to propose a simplified version of the B.C.S./F.C.S. system, and to explore the merits of using nonlinear recurrent dynamics. The biologically plausible model we propose is paralleled with classical computational vision techniques, while a link with the variational approach to computer vision is established. By interpreting the network's function in a probabilistic manner we derive an algorithm for learning the network weights using manually determined segmentations excerpted from the Berkeley database. This facilitates learning the terms involved in the variational criterion that quantifies edge map quality from ground truth data. Using the learned weights our network outperforms classical edge detection algorithms, when evaluated on the Berkeley segmentation benchmark.