  In this paper we introduce a novel Salience Affected Artificial Neural
Network (SANN) that models the way neuromodulators such as dopamine and
noradrenaline affect neural dynamics in the human brain by being distributed
diffusely through neocortical regions, allowing both salience signals to
modulate cognition immediately, and one time learning to take place through
strengthening entire patterns of activation at one go. We present a model that
is capable of one-time salience tagging in a neural network trained to classify
objects, and returns a salience response during classification (inference). We
explore the effects of salience on learning via its effect on the activation
functions of each node, as well as on the strength of weights between nodes in
the network. We demonstrate that salience tagging can improve classification
confidence for both the individual image as well as the class of images it
belongs to. We also show that the computation impact of producing a salience
response is minimal. This research serves as a proof of concept, and could be
the first step towards introducing salience tagging into Deep Learning Networks
and robotics.
