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 . This allows one-time learning to take place through strengthening entire patterns of activation at one go. We present a model that accepts asalience signal , and returns a reverse salience signal . We demonstrate that we can tag an image with salience with only a single training iteration, and that the same image will then produces the highest reverse salience signal during classification . 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  in the network. We demonstrate that a salience signal improves classification accuracy of the specific image that was tagged with salience, as well as all images in the same class , while penalizing images in other classes. Results are validated using 5-fold validation testing on MNIST and Fashion MNIST datasets . This research serves as a proof of concept, and could be the first step towards introducing salience tagging into Deep Learning Networks and robotics.
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.
R
. This allows one-time
, allowing both salience signals to modulate cognition immediately, and one time
D
accepts a
R
salience signal
is capable of one-time salience tagging in a neural network trained to classify objects
D
reverse salience signal
R
. We demonstrate that we can tag an image with salience with only a single training iteration, and that the same image will then produces the highest reverse salience signal during classification
salience response during classification (inference)
A
between nodes
R
a salience signal improves classification accuracy of the specific image that was tagged with salience,
salience tagging can improve classification confidence for both the individual image
R
all images in the same class , while penalizing images in other classes. Results are validated using 5-fold validation testing on MNIST and Fashion MNIST datasets
the class of images it belongs to. We also show that the computation impact of producing a salience response is minimal