In the context of visual systems for robots, we have made use of a high order gaussian synapses network and the Gaussian Synapses Backpropagation Algorithm (GSBP) for the implementation of the detectors that constitute one part of the whole visual architecture. These detectors are trained to be sensitive to spatial patterns that are relevant for the decisions the robot must perform during its operation in an environment. The inclusion of gaussian functions in the synapses of the network allows the network to select the appropriate spatial information and filter out all that is irrelevant according to the training it has received. In this paper, we will show how these networks are easily trained to ignore backgrounds. In addition, with a very simple training set and an appropriate input selection strategy, the networks detect objects independently of size and position. These systems, coupled with an attention mechanism result in a very efficient visual information processor.