Feedback connections are a prominent feature of cortical anatomy and are likely
to have significant functional role in neural information processing. We present
a neural network model of cortical feedback that successfully simulates
neurophysiological data associated with attention. In this domain our model can
be considered a more detailed, and biologically plausible, implementation of the
biased competition model of attention. However, our model is more general as it
can also explain a variety of other top-down processes in vision, such as
figure/ground segmentation and contextual cueing. This model thus suggests that
a common mechanism, involving cortical feedback pathways, is responsible for a
range of phenomena and provides a unified account of currently disparate areas
of research
