  Previous studies suggested that lateral interactions of V1 cells are
responsible, among other visual effects, of bottom-up visual attention
(alternatively named visual salience or saliency). Our objective is to mimic
these connections with a neurodynamic network of firing-rate neurons in order
to predict visual attention. Early visual subcortical processes (i.e. retinal
and thalamic) are functionally simulated. An implementation of the cortical
magnification function is included to define the retinotopical projections
towards V1, processing neuronal activity for each distinct view during scene
observation. Novel computational definitions of top-down inhibition (in terms
of inhibition of return and selection mechanisms), are also proposed to predict
attention in Free-Viewing and Visual Search tasks. Results show that our model
outpeforms other biologically-inpired models of saliency prediction while
predicting visual saccade sequences with the same model. We also show how
temporal and spatial characteristics of inhibition of return can improve
prediction of saccades, as well as how distinct search strategies (in terms of
feature-selective or category-specific inhibition) can predict attention at
distinct image contexts.
