  A convolution model which accounts for neural activity dynamics in the
primary visual cortex is derived and used to detect visually salient contours
in images. Image inputs to the model are modulated by long-range horizontal
connections, allowing contextual effects in the image to determine visual
saliency, i.e. line segments arranged in a closed contour elicit a larger
neural response than line segments forming background clutter. The model is
tested on 3 types of contour, including a line, a circular closed contour, and
a non-circular closed contour. Using a modified association field to describe
horizontal connections the model is found to perform well for different
parameter values. For each type of contour a different facilitation mechanism
is found. Operating as a feed-forward network, the model assigns saliency by
increasing the neural activity of line segments facilitated by the horizontal
connections. Alternatively, operating as a feedback network, the model can
achieve further improvement over several iterations through cooperative
interactions. This model has no dynamical stability issues, and is suitable for
use in biologically-inspired neural networks.
