We present an approach to modeling attention which originates in computational neuroscience. We aim at elaborating the underlying
mechanisms of attention by fitting the model with data from electrophysiology. Our strategy is to either confirm, reject,
modify or extend the model to accumulate knowledge in a single model across various experiments. Here, we demonstrate the
present state of the art and show that the model allows for a goal-directed search for an object in natural scenes.